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### 2017

• A. Borraccino, D. Schlipf, F. Haizmann, and R. Wagner, “Wind field reconstruction from nacelle-mounted lidar short-range measurements,” Wind energy science, vol. 2, iss. 1, pp. 269-283, 2017. doi:10.5194/wes-2-269-2017
Profiling nacelle lidars probe the wind at several heights and several distances upstream of the rotor. The development of such lidar systems is relatively recent, and it is still unclear how to condense the lidar raw measurements into useful wind field characteristics such as speed, direction, vertical and longitudinal gradients (wind shear). In this paper, we demonstrate an innovative method to estimate wind field characteristics using nacelle lidar measurements taken within the induction zone. Model-fitting wind field reconstruction techniques are applied to nacelle lidar measurements taken at multiple distances close to the rotor, where a wind model is combined with a simple induction model. The method allows robust determination of free-stream wind characteristics. The method was applied to experimental data obtained with two different types of nacelle lidar (five-beam Demonstrator and ZephIR Dual Mode). The reconstructed wind speed was within 0.5 % of the wind speed measured with a mast-top-mounted cup anemometer at 2.5 rotor diameters upstream of the turbine. The technique described in this paper overcomes measurement range limitations of the currently available nacelle lidar technology.

@Article{Borraccino2017,
Title = {Wind field reconstruction from nacelle-mounted lidar short-range measurements},
Author = {Borraccino, A. and Schlipf, D. and Haizmann, F. and Wagner, R.},
Journal = {Wind Energy Science},
Year = {2017},
Number = {1},
Pages = {269--283},
Volume = {2},
Abstract = {Profiling nacelle lidars probe the wind at several heights and several distances upstream of the rotor. The development of such lidar systems is relatively recent, and it is still unclear how to condense the lidar raw measurements into useful wind field characteristics such as speed, direction, vertical and longitudinal gradients (wind shear). In this paper, we demonstrate an innovative method to estimate wind field characteristics using nacelle lidar measurements taken within the induction zone. Model-fitting wind field reconstruction techniques are applied to nacelle lidar measurements taken at multiple distances close to the rotor, where a wind model is combined with a simple induction model. The method allows robust determination of free-stream wind characteristics. The method was applied to experimental data obtained with two different types of nacelle lidar (five-beam Demonstrator and ZephIR Dual Mode). The reconstructed wind speed was within 0.5 % of the wind speed measured with a mast-top-mounted cup anemometer at 2.5 rotor diameters upstream of the turbine. The technique described in this paper overcomes measurement range limitations of the currently available nacelle lidar technology.},
Doi = {10.5194/wes-2-269-2017},
Url = {http://dx.doi.org/10.5194/wes-2-269-2017}
}

• A. Choukulkar, W. A. Brewer, S. P. Sandberg, A. Weickmann, T. A. Bonin, R. M. Hardesty, J. K. Lundquist, R. Delgado, G. V. Iungo, R. Ashton, M. Debnath, L. Bianco, J. M. Wilczak, S. Oncley, and D. Wolfe, “Evaluation of single and multiple doppler lidar techniques to measure complex flow during the xpia field campaign,” Atmospheric measurement techniques, vol. 10, iss. 1, pp. 247-264, 2017. doi:10.5194/amt-10-247-2017
Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO, from 2 March to 31 May 2015 brought together a large suite of in situ and remote sensing measurement platforms to evaluate complex flow measurement strategies. In this paper, measurement uncertainties for different single and multi-Doppler strategies using simple scan geometries (conical, vertical plane and staring) are investigated. The tradeoffs (such as time–space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single-/multi-Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty. It was found that with an increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity along with horizontal homogeneity and less representative line-of-sight velocity statistics. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions.

@Article{Choukulkar2017,
Title = {Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign},
Author = {Choukulkar, A. and Brewer, W. A. and Sandberg, S. P. and Weickmann, A. and Bonin, T. A. and Hardesty, R. M. and Lundquist, J. K. and Delgado, R. and Iungo, G. V. and Ashton, R. and Debnath, M. and Bianco, L. and Wilczak, J. M. and Oncley, S. and Wolfe, D.},
Journal = {Atmospheric Measurement Techniques},
Year = {2017},
Number = {1},
Pages = {247--264},
Volume = {10},
Abstract = {Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO, from 2 March to 31 May 2015 brought together a large suite of in situ and remote sensing measurement platforms to evaluate complex flow measurement strategies.
In this paper, measurement uncertainties for different single and multi-Doppler strategies using simple scan geometries (conical, vertical plane and staring) are investigated. The tradeoffs (such as time–space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single-/multi-Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty.
It was found that with an increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity along with horizontal homogeneity and less representative line-of-sight velocity statistics. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions.},
Doi = {10.5194/amt-10-247-2017},
Url = {http://dx.doi.org/10.5194/amt-10-247-2017}
}

• M. Debnath, G. V. Iungo, A. W. Brewer, A. Choukulkar, R. Delgado, S. Gunter, J. K. Lundquist, J. L. Schroeder, J. M. Wilczak, and D. Wolfe, “Assessment of virtual towers performed with scanning wind lidars and ka-band radars during the XPIA experiment,” Atmospheric measurement techniques, vol. 10, iss. 3, pp. 1215-1227, 2017. doi:10.5194/amt-10-1215-2017
During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign, which was carried out at the Boulder Atmospheric Observatory (BAO) in spring 2015, multiple-Doppler scanning strategies were carried out with scanning wind lidars and Ka-band radars. Specifically, step–stare measurements were collected simultaneously with three scanning Doppler lidars, while two scanning Ka-band radars carried out simultaneous range height indicator (RHI) scans. The XPIA experiment provided the unique opportunity to compare directly virtual-tower measurements performed simultaneously with Ka-band radars and Doppler wind lidars. Furthermore, multiple-Doppler measurements were assessed against sonic anemometer data acquired from the meteorological tower (met-tower) present at the BAO site and a lidar wind profiler. This survey shows that – despite the different technologies, measurement volumes and sampling periods used for the lidar and radar measurements – a very good accuracy is achieved for both remote-sensing techniques for probing horizontal wind speed and wind direction with the virtual-tower scanning technique.

@Article{Debnath2017,
Title = {Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the {XPIA} experiment},
Author = {Mithu Debnath and Giacomo Valerio Iungo and W. Alan Brewer and Aditya Choukulkar and Ruben Delgado and Scott Gunter and Julie K. Lundquist and John L. Schroeder and James M. Wilczak and Daniel Wolfe},
Journal = {Atmospheric Measurement Techniques},
Year = {2017},
Month = {mar},
Number = {3},
Pages = {1215--1227},
Volume = {10},
Abstract = {During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign, which was carried out at the Boulder Atmospheric Observatory (BAO) in spring 2015, multiple-Doppler scanning strategies were carried out with scanning wind lidars and Ka-band radars. Specifically, step–stare measurements were collected simultaneously with three scanning Doppler lidars, while two scanning Ka-band radars carried out simultaneous range height indicator (RHI) scans. The XPIA experiment provided the unique opportunity to compare directly virtual-tower measurements performed simultaneously with Ka-band radars and Doppler wind lidars. Furthermore, multiple-Doppler measurements were assessed against sonic anemometer data acquired from the meteorological tower (met-tower) present at the BAO site and a lidar wind profiler. This survey shows that – despite the different technologies, measurement volumes and sampling periods used for the lidar and radar measurements – a very good accuracy is achieved for both remote-sensing techniques for probing horizontal wind speed and wind direction with the virtual-tower scanning technique.},
Doi = {10.5194/amt-10-1215-2017},
Publisher = {Copernicus {GmbH}},
Url = {http://dx.doi.org/10.5194/amt-10-1215-2017}
}

• G. Demurtas, T. Friis Pedersen, and R. Wagner, “Nacelle power curve measurement with spinner anemometer and uncertainty evaluation,” Wind energy science, vol. 2, iss. 1, pp. 97-114, 2017. doi:10.5194/wes-2-97-2017
The objective of this investigation was to verify the feasibility of using the spinner anemometer calibration and nacelle transfer function determined on one reference wind turbine, in order to assess the power performance of a second identical turbine. An experiment was set up with a met mast in a position suitable to measure the power curve of the two wind turbines, both equipped with a spinner anemometer. An IEC 61400-12-1-compliant power curve was then measured for both wind turbines using the met mast. The NTF (nacelle transfer function) was measured on the reference wind turbine and then applied to both turbines to calculate the free wind speed. For each of the two wind turbines, the power curve (PC) was measured with the met mast and the nacelle power curve (NPC) with the spinner anemometer. Four power curves (two PCs and two NPCs) were compared in terms of AEP (annual energy production) for a Rayleigh wind speed probability distribution. For each wind turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference wind turbine and within 0.38 % for the second wind turbine, for a mean wind speed of 8 m s−1.

@Article{Demurtas2017,
Title = {Nacelle power curve measurement with spinner anemometer and uncertainty evaluation},
Author = {Demurtas, G. and Friis Pedersen, T. and Wagner, R.},
Journal = {Wind Energy Science},
Year = {2017},
Number = {1},
Pages = {97--114},
Volume = {2},
Abstract = {The objective of this investigation was to verify the feasibility of using the spinner anemometer calibration and nacelle transfer function determined on one reference wind turbine, in order to assess the power performance of a second identical turbine. An experiment was set up with a met mast in a position suitable to measure the power curve of the two wind turbines, both equipped with a spinner anemometer. An IEC 61400-12-1-compliant power curve was then measured for both wind turbines using the met mast. The NTF (nacelle transfer function) was measured on the reference wind turbine and then applied to both turbines to calculate the free wind speed. For each of the two wind turbines, the power curve (PC) was measured with the met mast and the nacelle power curve (NPC) with the spinner anemometer. Four power curves (two PCs and two NPCs) were compared in terms of AEP (annual energy production) for a Rayleigh wind speed probability distribution. For each wind turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference wind turbine and within 0.38 % for the second wind turbine, for a mean wind speed of 8 m s−1.},
Doi = {10.5194/wes-2-97-2017},
Url = {http://dx.doi.org/10.5194/wes-2-97-2017}
}

• J. Gottschall, B. Gribben, D. Stein, and I. Würth, “Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity,” Wiley interdisciplinary reviews: energy and environment, 2017. doi:10.1002/wene.250
Floating lidar was introduced in 2009 as an offshore wind measurement technology focusing on the specific needs of the wind industry with regard to wind resource assessment applications. Floating lidar systems (FLS) are meant to replace an offshore met mast, being significantly cheaper and saving an essential part of project upfront investment costs. But at the same time, they need to overcome particular challenges—these are (1) the movement of the sea imparting motion on the buoy and the lidar, and the subsequent challenge of maintaining wind speed and direction accuracy, and (2) the remoteness of the deployed system in an extremely challenging environment necessitating robust, autonomous and reliable operation of measurement, power supply, data logging, and communication systems. The issue of motion influences was investigated in a number of studies and is to be checked and monitored in offshore trials of individual FLS realizations. In trials to date, such influences have been demonstrated to be negligibly or manageably small with the application of motion reduction or compensation strategies. Thereby, it is possible to achieve accurate wind measurement data from FLS. The second kind of challenge is tackled by implementing a sufficiently robust and reliable FLS design. Recommended practices collected by a working group within the International Energy Agency (IEA) Wind Task 32 and within the UK offshore wind accelerator program offer guidance for FLS design and configuration, and furthermore set requirements for trialing the system types and individual devices in representative offshore conditions.

@Article{Gottschall2017,
Title = {Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity},
Author = {Gottschall, Julia and Gribben, Brian and Stein, Detlef and Würth, Ines},
Journal = {Wiley Interdisciplinary Reviews: Energy and Environment},
Year = {2017},
Abstract = {Floating lidar was introduced in 2009 as an offshore wind measurement technology focusing on the specific needs of the wind industry with regard to wind resource assessment applications. Floating lidar systems (FLS) are meant to replace an offshore met mast, being significantly cheaper and saving an essential part of project upfront investment costs. But at the same time, they need to overcome particular challenges—these are (1) the movement of the sea imparting motion on the buoy and the lidar, and the subsequent challenge of maintaining wind speed and direction accuracy, and (2) the remoteness of the deployed system in an extremely challenging environment necessitating robust, autonomous and reliable operation of measurement, power supply, data logging, and communication systems. The issue of motion influences was investigated in a number of studies and is to be checked and monitored in offshore trials of individual FLS realizations. In trials to date, such influences have been demonstrated to be negligibly or manageably small with the application of motion reduction or compensation strategies. Thereby, it is possible to achieve accurate wind measurement data from FLS. The second kind of challenge is tackled by implementing a sufficiently robust and reliable FLS design. Recommended practices collected by a working group within the International Energy Agency (IEA) Wind Task 32 and within the UK offshore wind accelerator program offer guidance for FLS design and configuration, and furthermore set requirements for trialing the system types and individual devices in representative offshore conditions.},
Doi = {10.1002/wene.250},
Publisher = {Wiley Periodicals, Inc.},
Url = {http://dx.doi.org/10.1002/wene.250}
}

• J. Mann, N. Angelou, J. Arnqvist, D. Callies, E. Cantero, C. R. Arroyo, M. Courtney, J. Cuxart, E. Dellwik, J. Gottschall, S. Ivanell, P. Kühn, G. Lea, J. C. Matos, J. M. L. M. Palma, L. Pauscher, A. Peña, S. J. Rodrigo, S. Söderberg, N. Vasiljevic, and V. C. Rodrigues, “Complex terrain experiments in the new european wind atlas,” Philosophical transactions of the royal society of london a: mathematical, physical and engineering sciences, vol. 375, iss. 2091, 2017. doi:10.1098/rsta.2016.0101
The New European Wind Atlas project will create a freely accessible wind atlas covering Europe and Turkey, develop the model chain to create the atlas and perform a series of experiments on flow in many different kinds of complex terrain to validate the models. This paper describes the experiments of which some are nearly completed while others are in the planning stage. All experiments focus on the flow properties that are relevant for wind turbines, so the main focus is the mean flow and the turbulence at heights between 40 and 300 m. Also extreme winds, wind shear and veer, and diurnal and seasonal variations of the wind are of interest. Common to all the experiments is the use of Doppler lidar systems to supplement and in some cases replace completely meteorological towers. Many of the lidars will be equipped with scan heads that will allow for arbitrary scan patterns by several synchronized systems. Two pilot experiments, one in Portugal and one in Germany, show the value of using multiple synchronized, scanning lidar, both in terms of the accuracy of the measurements and the atmospheric physical processes that can be studied. The experimental data will be used for validation of atmospheric flow models and will by the end of the project be freely available.This article is part of the themed issue {\textquoteleft}Wind energy in complex terrains{\textquoteright}.

@Article{Mann2017,
Title = {Complex terrain experiments in the New European Wind Atlas},
Author = {Mann, J. and Angelou, N. and Arnqvist, J. and Callies, D. and Cantero, E. and Arroyo, R. Ch{\'a}vez and Courtney, M. and Cuxart, J. and Dellwik, E. and Gottschall, J. and Ivanell, S. and K{\"u}hn, P. and Lea, G. and Matos, J. C. and Palma, J. M. L. M. and Pauscher, L. and Pe{\~n}a, A. and Rodrigo, J. Sanz and S{\"o}derberg, S. and Vasiljevic, N. and Rodrigues, C. Veiga},
Journal = {Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences},
Year = {2017},
Number = {2091},
Volume = {375},
Abstract = {The New European Wind Atlas project will create a freely accessible wind atlas covering Europe and Turkey, develop the model chain to create the atlas and perform a series of experiments on flow in many different kinds of complex terrain to validate the models. This paper describes the experiments of which some are nearly completed while others are in the planning stage. All experiments focus on the flow properties that are relevant for wind turbines, so the main focus is the mean flow and the turbulence at heights between 40 and 300 m. Also extreme winds, wind shear and veer, and diurnal and seasonal variations of the wind are of interest. Common to all the experiments is the use of Doppler lidar systems to supplement and in some cases replace completely meteorological towers. Many of the lidars will be equipped with scan heads that will allow for arbitrary scan patterns by several synchronized systems. Two pilot experiments, one in Portugal and one in Germany, show the value of using multiple synchronized, scanning lidar, both in terms of the accuracy of the measurements and the atmospheric physical processes that can be studied. The experimental data will be used for validation of atmospheric flow models and will by the end of the project be freely available.This article is part of the themed issue {\textquoteleft}Wind energy in complex terrains{\textquoteright}.},
Doi = {10.1098/rsta.2016.0101},
Publisher = {The Royal Society},
Url = {http://dx.doi.org/10.1098/rsta.2016.0101}
}

• A. R. Meyer Forsting, N. Troldborg, J. P. Murcia Leon, A. Sathe, N. Angelou, and A. Vignaroli, “Validation of a cfd model with a synchronized triple-lidar system in the wind turbine induction zone,” Wind energy, 2017. doi:10.1002/we.2103
A novel validation methodology allows verifying a CFD model over the entire wind turbine induction zone using measurements from three synchronized lidars. The validation procedure relies on spatially discretizing the probability density function of the measured free-stream wind speed. The resulting distributions are reproduced numerically by weighting steady-state Reynolds averaged Navier-Stokes simulations accordingly. The only input varying between these computations is the velocity at the inlet boundary. The rotor is modelled using an actuator disc. So as to compare lidar and simulations, the spatial and temporal uncertainty of the measurements is quantified and propagated through the data processing. For all velocity components the maximal difference between measurements and model are below 4.5% relative to the average wind speed for most of the validation space. This applies to both mean and standard deviation. One rotor radius upstream the difference reaches maximally 1.3% for the axial component.

@Article{Meyer Forsting2017,
Title = {Validation of a CFD model with a synchronized triple-lidar system in the wind turbine induction zone},
Author = {Meyer Forsting, A. R. and Troldborg, N. and Murcia Leon, J. P. and Sathe, A. and Angelou, N. and Vignaroli, A.},
Journal = {Wind Energy},
Year = {2017},
Abstract = {A novel validation methodology allows verifying a CFD model over the entire wind turbine induction zone using measurements from three synchronized lidars. The validation procedure relies on spatially discretizing the probability density function of the measured free-stream wind speed. The resulting distributions are reproduced numerically by weighting steady-state Reynolds averaged Navier-Stokes simulations accordingly. The only input varying between these computations is the velocity at the inlet boundary. The rotor is modelled using an actuator disc. So as to compare lidar and simulations, the spatial and temporal uncertainty of the measurements is quantified and propagated through the data processing. For all velocity components the maximal difference between measurements and model are below 4.5% relative to the average wind speed for most of the validation space. This applies to both mean and standard deviation. One rotor radius upstream the difference reaches maximally 1.3% for the axial component.},
Doi = {10.1002/we.2103},
Url = {http://dx.doi.org/10.1002/we.2103}
}

• R. K. Newsom, A. W. Brewer, J. M. Wilczak, D. E. Wolfe, S. P. Oncley, and J. K. Lundquist, “Validating precision estimates in horizontal wind measurements from a doppler lidar,” Atmospheric measurement techniques, vol. 10, iss. 3, pp. 1229-1240, 2017. doi:10.5194/amt-10-1229-2017
Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower. All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.

@Article{Newsom2017,
Title = {Validating precision estimates in horizontal wind measurements from a Doppler lidar},
Author = {Rob K. Newsom and W. Alan Brewer and James M. Wilczak and Daniel E. Wolfe and Steven P. Oncley and Julie K. Lundquist},
Journal = {Atmospheric Measurement Techniques},
Year = {2017},
Month = {mar},
Number = {3},
Pages = {1229--1240},
Volume = {10},
Abstract = {Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.
All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.},
Doi = {10.5194/amt-10-1229-2017},
Publisher = {Copernicus {GmbH}},
Url = {http://dx.doi.org/10.5194/amt-10-1229-2017}
}

• A. Peña, J. Mann, and N. Dimitrov, “Turbulence characterization from a forward-looking nacelle lidar,” Wind energy science, vol. 2, iss. 1, pp. 133-152, 2017. doi:10.5194/wes-2-133-2017
We present two methods to characterize turbulence in the turbine inflow using radial velocity measurements from nacelle-mounted lidars. The first uses a model of the three-dimensional spectral velocity tensor combined with a model of the spatial radial velocity averaging of the lidars, and the second uses the ensemble-averaged Doppler radial velocity spectrum. With the former, filtered turbulence estimates can be predicted, whereas the latter model-free method allows us to estimate unfiltered turbulence measures. Two types of forward-looking nacelle lidars are investigated: a pulsed system that uses a five-beam configuration and a continuous-wave system that scans conically. For both types of lidars, we show how the radial velocity spectra of the lidar beams are influenced by turbulence characteristics, and how to extract the velocity-tensor parameters that are useful to predict the loads on a turbine. We also show how the velocity-component variances and co-variances can be estimated from the radial-velocity unfiltered variances of the lidar beams. We demonstrate the methods using measurements from an experiment conducted at the Nørrekær Enge wind farm in northern Denmark, where both types of lidars were installed on the nacelle of a wind turbine. Comparison of the lidar-based along-wind unfiltered variances with those from a cup anemometer installed on a meteorological mast close to the turbine shows a bias of just 2 %. The ratios of the unfiltered and filtered radial velocity variances of the lidar beams to the cup-anemometer variances are well predicted by the spectral model. However, other lidar-derived estimates of velocity-component variances and co-variances do not agree with those from a sonic anemometer on the mast, which we mostly attribute to the small cone angle of the lidar. The velocity-tensor parameters derived from sonic-anemometer velocity spectra and those derived from lidar radial velocity spectra agree well under both near-neutral atmospheric stability and high wind-speed conditions, with differences increasing with decreasing wind speed and increasing stability. We also partly attribute these differences to the lidar beam configuration.

@Article{Pena2017,
Title = {Turbulence characterization from a forward-looking nacelle lidar},
Author = {Peña, A. and Mann, J. and Dimitrov, N.},
Journal = {Wind Energy Science},
Year = {2017},
Number = {1},
Pages = {133--152},
Volume = {2},
Abstract = {We present two methods to characterize turbulence in the turbine inflow using radial velocity measurements from nacelle-mounted lidars. The first uses a model of the three-dimensional spectral velocity tensor combined with a model of the spatial radial velocity averaging of the lidars, and the second uses the ensemble-averaged Doppler radial velocity spectrum. With the former, filtered turbulence estimates can be predicted, whereas the latter model-free method allows us to estimate unfiltered turbulence measures. Two types of forward-looking nacelle lidars are investigated: a pulsed system that uses a five-beam configuration and a continuous-wave system that scans conically. For both types of lidars, we show how the radial velocity spectra of the lidar beams are influenced by turbulence characteristics, and how to extract the velocity-tensor parameters that are useful to predict the loads on a turbine. We also show how the velocity-component variances and co-variances can be estimated from the radial-velocity unfiltered variances of the lidar beams. We demonstrate the methods using measurements from an experiment conducted at the Nørrekær Enge wind farm in northern Denmark, where both types of lidars were installed on the nacelle of a wind turbine. Comparison of the lidar-based along-wind unfiltered variances with those from a cup anemometer installed on a meteorological mast close to the turbine shows a bias of just 2 %. The ratios of the unfiltered and filtered radial velocity variances of the lidar beams to the cup-anemometer variances are well predicted by the spectral model. However, other lidar-derived estimates of velocity-component variances and co-variances do not agree with those from a sonic anemometer on the mast, which we mostly attribute to the small cone angle of the lidar. The velocity-tensor parameters derived from sonic-anemometer velocity spectra and those derived from lidar radial velocity spectra agree well under both near-neutral atmospheric stability and high wind-speed conditions, with differences increasing with decreasing wind speed and increasing stability. We also partly attribute these differences to the lidar beam configuration.},
Doi = {10.5194/wes-2-133-2017},
Url = {http://dx.doi.org/10.5194/wes-2-133-2017}
}

• S. Raach, D. Schlipf, and P. W. Cheng, “Lidar-based wake tracking for closed-loop wind farm control,” Wind energy science, vol. 2, iss. 1, pp. 257-267, 2017. doi:10.5194/wes-2-257-2017
This work presents two advancements towards closed-loop wake redirection of a wind turbine. First, a model-based wake-tracking approach is presented, which uses a nacelle-based lidar system facing downwind to obtain information about the wake. The method uses a reduced-order wake model to track the wake. The wake tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a simulation with the Simulator fOr Wind Farm Applications (SOWFA). Second, a controller for closed-loop wake steering is presented. It uses the wake-tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, the two approaches enable a closed-loop wake redirection.

@Article{Raach2017,
Title = {Lidar-based wake tracking for closed-loop wind farm control},
Author = {Raach, S. and Schlipf, D. and Cheng, P. W.},
Journal = {Wind Energy Science},
Year = {2017},
Number = {1},
Pages = {257--267},
Volume = {2},
Abstract = {This work presents two advancements towards closed-loop wake redirection of a wind turbine. First, a model-based wake-tracking approach is presented, which uses a nacelle-based lidar system facing downwind to obtain information about the wake. The method uses a reduced-order wake model to track the wake. The wake tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a simulation with the Simulator fOr Wind Farm Applications (SOWFA). Second, a controller for closed-loop wake steering is presented. It uses the wake-tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, the two approaches enable a closed-loop wake redirection.},
Doi = {10.5194/wes-2-257-2017},
Url = {http://dx.doi.org/10.5194/wes-2-257-2017}
}

### 2016

• A. Borraccino, M. Courtney, and R. Wagner, “Generic methodology for field calibration of nacelle-based wind lidars,” Remote sensing, vol. 8, iss. 11, 2016. doi:10.3390/rs8110907
Nacelle-based Doppler wind lidars have shown promising capabilities to assess power performance, detect yaw misalignment or perform feed-forward control. The power curve application requires uncertainty assessment. Traceable measurements and uncertainties of nacelle-based wind lidars can be obtained through a methodology applicable to any type of existing and upcoming nacelle lidar technology. The generic methodology consists in calibrating all the inputs of the wind field reconstruction algorithms of a lidar. These inputs are the line-of-sight velocity and the beam position, provided by the geometry of the scanning trajectory and the lidar inclination. The line-of-sight velocity is calibrated in atmospheric conditions by comparing it to a reference quantity based on classic instrumentation such as cup anemometers and wind vanes. The generic methodology was tested on two commercially developed lidars, one continuous wave and one pulsed systems, and provides consistent calibration results: linear regressions show a difference of ∼0.5% between the lidar-measured and reference line-of-sight velocities. A comprehensive uncertainty procedure propagates the reference uncertainty to the lidar measurements. At a coverage factor of two, the estimated line-of-sight velocity uncertainty ranges from 3.2% at 3 m ⋅ s −1 to 1.9% at 16 m ⋅ s −1 . Most of the line-of-sight velocity uncertainty originates from the reference: the cup anemometer uncertainty accounts for ∼90% of the total uncertainty. The propagation of uncertainties to lidar-reconstructed wind characteristics can use analytical methods in simple cases, which we demonstrate through the example of a two-beam system. The newly developed calibration methodology allows robust evaluation of a nacelle lidar’s performance and uncertainties to be established. Calibrated nacelle lidars may consequently be further used for various wind turbine applications in confidence.

@Article{Borraccino2016,
Title = {Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars},
Author = {Borraccino, Antoine and Courtney, Michael and Wagner, Rozenn},
Journal = {Remote Sensing},
Year = {2016},
Number = {11},
Volume = {8},
Abstract = {Nacelle-based Doppler wind lidars have shown promising capabilities to assess power performance, detect yaw misalignment or perform feed-forward control. The power curve application requires uncertainty assessment. Traceable measurements and uncertainties of nacelle-based wind lidars can be obtained through a methodology applicable to any type of existing and upcoming nacelle lidar technology. The generic methodology consists in calibrating all the inputs of the wind field reconstruction algorithms of a lidar. These inputs are the line-of-sight velocity and the beam position, provided by the geometry of the scanning trajectory and the lidar inclination. The line-of-sight velocity is calibrated in atmospheric conditions by comparing it to a reference quantity based on classic instrumentation such as cup anemometers and wind vanes. The generic methodology was tested on two commercially developed lidars, one continuous wave and one pulsed systems, and provides consistent calibration results: linear regressions show a difference of ∼0.5% between the lidar-measured and reference line-of-sight velocities. A comprehensive uncertainty procedure propagates the reference uncertainty to the lidar measurements. At a coverage factor of two, the estimated line-of-sight velocity uncertainty ranges from 3.2% at 3 m ⋅ s −1 to 1.9% at 16 m ⋅ s −1 . Most of the line-of-sight velocity uncertainty originates from the reference: the cup anemometer uncertainty accounts for ∼90% of the total uncertainty. The propagation of uncertainties to lidar-reconstructed wind characteristics can use analytical methods in simple cases, which we demonstrate through the example of a two-beam system. The newly developed calibration methodology allows robust evaluation of a nacelle lidar’s performance and uncertainties to be established. Calibrated nacelle lidars may consequently be further used for various wind turbine applications in confidence.},
Doi = {10.3390/rs8110907},
Url = {http://dx.doi.org/10.3390/rs8110907}
}

• R. Bos, A. Giyanani, and W. Bierbooms, “Assessing the severity of wind gusts with lidar,” Remote sensing, vol. 8, iss. 9, p. 758, 2016. doi:10.3390/rs8090758
Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine’s shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation.

@Article{Bos2016,
Title = {Assessing the Severity of Wind Gusts with Lidar},
Author = {Bos, René and Giyanani, Ashim and Bierbooms, Wim},
Journal = {Remote Sensing},
Year = {2016},
Number = {9},
Pages = {758},
Volume = {8},
Abstract = {Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine’s shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation.},
Doi = {10.3390/rs8090758},
Url = {http://www.mdpi.com/2072-4292/8/9/758}
}

• M. Churchfield, Q. Wang, A. Scholbrock, T. Herges, T. Mikkelsen, and M. Sjöholm, “Using high-fidelity computational fluid dynamics to help design a wind turbine wake measurement experiment,” Journal of physics: conference series, vol. 753, iss. 3, p. 32009, 2016. doi:10.1088/1742-6596/753/3/032009
We describe the process of using large-eddy simulations of wind turbine wake flow to help design a wake measurement campaign. The main goal of the experiment is to measure wakes and wake deflection that result from intentional yaw misalignment under a variety of atmospheric conditions at the Scaled Wind Farm Technology facility operated by Sandia National Laboratories in Lubbock, Texas. Prior simulation studies have shown that wake deflection may be used for wind-plant control that maximizes plant power output. In this study, simulations are performed to characterize wake deflection and general behavior before the experiment is performed to ensure better upfront planning. Beyond characterizing the expected wake behavior, we also use the large-eddy simulation to test a virtual version of the lidar we plan to use to measure the wake and better understand our lidar scan strategy options. This work is an excellent example of a “simulation-in-the-loop” measurement campaign.

@Article{Churchfield2016,
Title = {Using High-Fidelity Computational Fluid Dynamics to Help Design a Wind Turbine Wake Measurement Experiment},
Author = {M Churchfield and Q Wang and A Scholbrock and T Herges and T Mikkelsen and M Sjöholm},
Journal = {Journal of Physics: Conference Series},
Year = {2016},
Number = {3},
Pages = {032009},
Volume = {753},
Abstract = {We describe the process of using large-eddy simulations of wind turbine wake flow to help design a wake measurement campaign. The main goal of the experiment is to measure wakes and wake deflection that result from intentional yaw misalignment under a variety of atmospheric conditions at the Scaled Wind Farm Technology facility operated by Sandia National Laboratories in Lubbock, Texas. Prior simulation studies have shown that wake deflection may be used for wind-plant control that maximizes plant power output. In this study, simulations are performed to characterize wake deflection and general behavior before the experiment is performed to ensure better upfront planning. Beyond characterizing the expected wake behavior, we also use the large-eddy simulation to test a virtual version of the lidar we plan to use to measure the wake and better understand our lidar scan strategy options. This work is an excellent example of a “simulation-in-the-loop” measurement campaign.},
Doi = {10.1088/1742-6596/753/3/032009},
Url = {https://doi.org/10.1088/1742-6596/753/3/032009}
}

• A. Cool G, “Floating lidar technology: oceanographic parameters influencing accuracy of wind vector reconstruction,” Master Thesis, 2016.
Wind assessment of new locations prior to the installation of wind turbines is an established process. However, this process is still very costly. The common way to measure offshore wind profiles is the usage of met masts, which get more expensive with increasing water depth and hub height. Recently, wind measurement LiDARs are installed on floating platforms to investigate the wind profile offshore. Floating LiDARs are a promising technology thanks to low cost and high flexibility. However, further understanding is required in order to make floating LiDAR technology fully acceptable. The movement of a floating LiDAR brings some uncertainty in reconstructing the wind vector and turbulence characteristics. These uncertainties hold back the full commercial acceptance of LiDAR technology in the offshore wind energy industry. In order to accept floating LiDARs as a valid technology, acceptance criteria need to be defined in terms of accuracy and confidence of the results. This thesis has developed a simulation tool to define and model the effect of uncertainties in floating Li- DAR technology, which influence the accuracy of the wind field characterisation. The model reconstructs wind vectors as seen by a specified LiDAR and is capable of analyzing the parameters influencing wind vector reconstruction. These parameters can be categorized in wave, wind and LiDAR conditions. Several wind profiles were used as input: a logarithmic wind profile for a first assessment, synthetic turbulent wind fields and Large Eddy simulations (LES) for a more realistic approach. Wave conditions based on the Airy wave and real-motion data are used to see the influence of wave height, wave period and wave number. It has been observed that floating LiDARs can reconstruct 10min velocities in a very accurate way, regardless of the experienced wave conditions which varied from normal to extreme conditions. For all considered wind tools, the error was less than 0.1 m/s for the averaged 10min wind speed, with a very small standard deviation, ¾. Reconstructing turbulence characteristics has been proven to be less accurate. The error is significant and cannot be ignored. With a reference turbulence intensity value of 8% at an altitude of 100m, the average bias can go up to 0.60% with a ¾-value of 0.52%for synthetic wind fields. In more extreme wave conditions, the average bias can go up to 1 % andmore. An error of more than 3%may occur at altitudes of 150m or higher for the turbulence intensity when using LES files in these extreme conditions. The use of motion correction is suggested to reduce this bias. This correction can happen with a mechanical system (motion stabilization platform) or a correction algorithm built into the software. Since the approach that was used in this research has proven to be successful, further research can be performed based on this study. Further investigation should be focused on understanding turbulence behaviour, measured by floating LiDARS. Also the usage of wind data coming from measurement campaigns should be valuable to proceed with in this research.

@MastersThesis{Cool2016,
Title = {Floating LiDAR Technology: Oceanographic parameters influencing accuracy of wind vector reconstruction},
Author = {Cool, G, A,},
School = {TU Delft},
Year = {2016},
Abstract = {Wind assessment of new locations prior to the installation of wind turbines is an established process. However, this process is still very costly. The common way to measure offshore wind profiles is the usage of met masts, which get more expensive with increasing water depth and hub height. Recently, wind measurement LiDARs are installed on floating platforms to investigate the wind profile offshore. Floating LiDARs are a promising technology thanks to low cost and high flexibility. However, further understanding is required in order to make floating LiDAR technology fully acceptable. The movement of a floating LiDAR brings some uncertainty in reconstructing the wind vector and turbulence characteristics. These uncertainties hold back the full commercial acceptance of LiDAR technology in the offshore wind energy industry. In order to accept floating LiDARs as a valid technology, acceptance criteria need to be defined in terms of accuracy and confidence of the results. This thesis has developed a simulation tool to define and model the effect of uncertainties in floating Li- DAR technology, which influence the accuracy of the wind field characterisation. The model reconstructs wind vectors as seen by a specified LiDAR and is capable of analyzing the parameters influencing wind vector reconstruction. These parameters can be categorized in wave, wind and LiDAR conditions. Several wind profiles were used as input: a logarithmic wind profile for a first assessment, synthetic turbulent wind fields and Large Eddy simulations (LES) for a more realistic approach. Wave conditions based on the Airy wave and real-motion data are used to see the influence of wave height, wave period and wave number. It has been observed that floating LiDARs can reconstruct 10min velocities in a very accurate way, regardless of the experienced wave conditions which varied from normal to extreme conditions. For all considered wind tools, the error was less than 0.1 m/s for the averaged 10min wind speed, with a very small standard deviation, ¾. Reconstructing turbulence characteristics has been proven to be less accurate. The error is significant and cannot be ignored. With a reference turbulence intensity value of 8% at an altitude of 100m, the average bias can go up to 0.60% with a ¾-value of 0.52%for synthetic wind fields. In more extreme wave conditions, the average bias can go up to 1 % andmore. An error of more than 3%may occur at altitudes of 150m or higher for the turbulence intensity when using LES files in these extreme conditions. The use of motion correction is suggested to reduce this bias. This correction can happen with a mechanical system (motion stabilization platform) or a correction algorithm built into the software. Since the approach that was used in this research has proven to be successful, further research can be performed based on this study. Further investigation should be focused on understanding turbulence behaviour, measured by floating LiDARS. Also the usage of wind data coming from measurement campaigns should be valuable to proceed with in this research.},
}

• D. Kim, T. Kim, G. Oh, J. Huh, and K. Ko, “A comparison of ground-based lidar and met mast wind measurements for wind resource assessment over various terrain conditions,” Journal of wind engineering and industrial aerodynamics, vol. 158, pp. 109-121, 2016. doi:10.1016/j.jweia.2016.09.011
Abstract In order to assess reliability of measurements from LiDAR (Light Detection and Ranging), a measurement campaign was led using ground-based LiDAR of \{WINDCUBE\} \{V2\} and meteorological masts at three measurement sites: Sumang, Gangjeong, and Susan, on Jeju Island, Korea. Each site had a different topographical complexity, which was evaluated by using a Ruggedness Index (RIX). Wind data was collected for 11−14 days from four heights on each site’s met mast. Data filtering was done to ensure data comparability between LiDAR and wind sensors. Analyses of LiDAR error, standard deviation, turbulence intensity and LiDAR error rate were conducted on data coming from each site. Also, the \{CFD\} analysis was performed at Sumang with the highest RIX. As a result, the concurrent wind measurement slopes were all close to one based on linear regression analysis. The coefficient of determination was almost all more than 0.9 for all heights at each site. LiDAR error rates for the measurement sites ranged approximately between 2% and 6%. The result of the \{CFD\} analysis showed that the depression was formed between two parasitic cones, between which the measurement point of Sumang was located, which led to greater positive LiDAR error.

@Article{Kim2016,
Title = {A comparison of ground-based LiDAR and met mast wind measurements for wind resource assessment over various terrain conditions },
Author = {Daeyoung Kim and Taewan Kim and Gwanjun Oh and Jongchul Huh and Kyungnam Ko},
Journal = {Journal of Wind Engineering and Industrial Aerodynamics },
Year = {2016},
Pages = {109 - 121},
Volume = {158},
Abstract = {Abstract In order to assess reliability of measurements from LiDAR (Light Detection and Ranging), a measurement campaign was led using ground-based LiDAR of \{WINDCUBE\} \{V2\} and meteorological masts at three measurement sites: Sumang, Gangjeong, and Susan, on Jeju Island, Korea. Each site had a different topographical complexity, which was evaluated by using a Ruggedness Index (RIX). Wind data was collected for 11−14 days from four heights on each site's met mast. Data filtering was done to ensure data comparability between LiDAR and wind sensors. Analyses of LiDAR error, standard deviation, turbulence intensity and LiDAR error rate were conducted on data coming from each site. Also, the \{CFD\} analysis was performed at Sumang with the highest RIX. As a result, the concurrent wind measurement slopes were all close to one based on linear regression analysis. The coefficient of determination was almost all more than 0.9 for all heights at each site. LiDAR error rates for the measurement sites ranged approximately between 2% and 6%. The result of the \{CFD\} analysis showed that the depression was formed between two parasitic cones, between which the measurement point of Sumang was located, which led to greater positive LiDAR error.},
Doi = {10.1016/j.jweia.2016.09.011},
Url = {https://doi.org/10.1016/j.jweia.2016.09.011}
}

• J. K. Lundquist, J. M. Wilczak, R. Ashton, L. Bianco, A. W. Brewer, A. Choukulkar, A. Clifton, M. Debnath, R. Delgado, K. Friedrich, S. Gunter, A. Hamidi, G. V. Iungo, A. Kaushik, B. Kosović, P. Langan, A. Lass, E. Lavin, J. C. -Y. Lee, K. L. McCaffrey, R. K. Newsom, D. C. Noone, S. P. Oncley, P. T. Quelet, S. P. Sandberg, J. L. Schroeder, W. J. Shaw, L. Sparling, C. S. Martin, A. S. Pe, E. Strobach, K. Tay, B. J. Vanderwende, A. Weickmann, D. Wolfe, and R. Worsnop, “Assessing state-of-the-art capabilities for probing the atmospheric boundary layer: the xpia field campaign,” Bulletin of the american meteorological society, 2016. doi:10.1175/BAMS-D-15-00151.1
AbstractTo assess current capabilities for measuring flow within the atmospheric boundary layer, including within wind farms, the U.S. Dept. of Energy sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign at the Boulder Atmospheric Observatory (BAO) in spring 2015. Herein, we summarize the XPIA field experiment, highlight novel measurement approaches, and quantify uncertainties associated with these measurement methods. Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipitation events, but they struggle at times to provide data during periods with limited atmospheric scatterers. In contrast, for the deployment geometry tested here, the lidars have slower scan rates and less range, but provide more data during non-precipitating atmospheric conditions. Microwave radiometers provide temperature profiles with approximately the same uncertainty as Radio-Acoustic Sounding Systems (RASS). Using a motion platform, we assess motion-compensation algorithms for lidars to be mounted on offshore platforms. Finally, we highlight cases for validation of mesoscale or large-eddy simulations, providing information on accessing the archived dataset. We conclude that modern remote sensing systems provide a generational improvement in observational capabilities, enabling resolution of fine-scale processes critical to understanding inhomogeneous boundary-layer flows.

@Article{Lundquist2016,
Title = {Assessing state-of-the-art capabilities for probing the atmospheric boundary layer: the XPIA field campaign},
Author = {Julie K. Lundquist and James M. Wilczak and Ryan Ashton and Laura Bianco and W. Alan Brewer and Aditya Choukulkar and Andrew Clifton and Mithu Debnath and Ruben Delgado and Katja Friedrich and Scott Gunter and Armita Hamidi and Giacomo Valerio Iungo and Aleya Kaushik and Branko Kosović and Patrick Langan and Adam Lass and Evan Lavin and Joseph C.-Y. Lee and Katherine L. McCaffrey and Rob K. Newsom and David C. Noone and Steven P. Oncley and Paul T. Quelet and Scott P. Sandberg and John L. Schroeder and William J. Shaw and Lynn Sparling and Clara St. Martin and Alexandra St. Pe and Edward Strobach and Ken Tay and Brian J. Vanderwende and Ann Weickmann and Daniel Wolfe and Rochelle Worsnop},
Journal = {Bulletin of the American Meteorological Society},
Year = {2016},
Abstract = {AbstractTo assess current capabilities for measuring flow within the atmospheric boundary layer, including within wind farms, the U.S. Dept. of Energy sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign at the Boulder Atmospheric Observatory (BAO) in spring 2015. Herein, we summarize the XPIA field experiment, highlight novel measurement approaches, and quantify uncertainties associated with these measurement methods. Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipitation events, but they struggle at times to provide data during periods with limited atmospheric scatterers. In contrast, for the deployment geometry tested here, the lidars have slower scan rates and less range, but provide more data during non-precipitating atmospheric conditions. Microwave radiometers provide temperature profiles with approximately the same uncertainty as Radio-Acoustic Sounding Systems (RASS). Using a motion platform, we assess motion-compensation algorithms for lidars to be mounted on offshore platforms. Finally, we highlight cases for validation of mesoscale or large-eddy simulations, providing information on accessing the archived dataset. We conclude that modern remote sensing systems provide a generational improvement in observational capabilities, enabling resolution of fine-scale processes critical to understanding inhomogeneous boundary-layer flows.},
Doi = {10.1175/BAMS-D-15-00151.1},
Url = {http://dx.doi.org/10.1175/BAMS-D-15-00151.1}
}

• M. Mirzaei and M. H. Hansen, “A lidar-assisted model predictive controller added on a traditional wind turbine controller,” in Proceedings of the american control conference, Boston, MA, USA, 2016. doi:10.1109/ACC.2016.7525110
LIDAR-assisted collective pitch control shows promising results for load reduction in the full load operating region of horizontal axis wind turbines (WT). Utilizing LIDARs in WT control can be approached in different ways; One method is to design the WT controller from ground up based on the LIDAR measurements. Nevertheless, to make the LIDAR-assisted controller easily implementable on existing wind turbines, one can design a controller that is added to the original and existing WT controller. This add-on solution makes it easier to prove the applicability and performance of the LIDAR-assisted WT control and opens the market of retrofitting existing wind turbines with the new technology. In this paper, we suggest a model predictive controller (MPC) that is added to the basic gain scheduled PI controller of a WT to enhance the performance of the closed loop system using LIDAR measurements. The performance of the MPC controller is compared against two controllers. The controllers are 1) a gain scheduled PI controller and 2) a controller with the same feedback as controller no. 1 and an added feed-forward loop (FF+PI controller). Simulations are used to compare their performances. The simulation scenarios include the extreme operating gust and normal power production using stochastic wind field in the full load region. The results show superior performance compared to the PI controller and a performance marginally better compared to the FF+PI controller. The reason for a better performance against the PI controller is that the MPC controller employs the LIDAR wind speed measurements to predict and compensate future disturbances. The MPC controller is designed based on the closed loop model of the wind turbine including the pitch actuator and therefore an appropriate pitch signal is calculated, while the FF+PI controller employs filter and delay compensation to take the actuator dynamics into account.

@InProceedings{Mirzaei2016,
Title = {A LIDAR-assisted model predictive controller added on a traditional wind turbine controller},
Author = {M. Mirzaei and M. H. Hansen},
Booktitle = {Proceedings of the American Control Conference},
Year = {2016},
Abstract = {LIDAR-assisted collective pitch control shows promising results for load reduction in the full load operating region of horizontal axis wind turbines (WT). Utilizing LIDARs in WT control can be approached in different ways; One method is to design the WT controller from ground up based on the LIDAR measurements. Nevertheless, to make the LIDAR-assisted controller easily implementable on existing wind turbines, one can design a controller that is added to the original and existing WT controller. This add-on solution makes it easier to prove the applicability and performance of the LIDAR-assisted WT control and opens the market of retrofitting existing wind turbines with the new technology. In this paper, we suggest a model predictive controller (MPC) that is added to the basic gain scheduled PI controller of a WT to enhance the performance of the closed loop system using LIDAR measurements. The performance of the MPC controller is compared against two controllers. The controllers are 1) a gain scheduled PI controller and 2) a controller with the same feedback as controller no. 1 and an added feed-forward loop (FF+PI controller). Simulations are used to compare their performances. The simulation scenarios include the extreme operating gust and normal power production using stochastic wind field in the full load region. The results show superior performance compared to the PI controller and a performance marginally better compared to the FF+PI controller. The reason for a better performance against the PI controller is that the MPC controller employs the LIDAR wind speed measurements to predict and compensate future disturbances. The MPC controller is designed based on the closed loop model of the wind turbine including the pitch actuator and therefore an appropriate pitch signal is calculated, while the FF+PI controller employs filter and delay compensation to take the actuator dynamics into account.},
Doi = {10.1109/ACC.2016.7525110},
Url = {http://dx.doi.org/10.1109/ACC.2016.7525110}
}

• J. F. Newman and A. Clifton, “Quantifying the effect of lidar turbulence error on wind power prediction,” in Poster presented at the american meteorological society (ams) 96th annual meeting, 2016.
Currently, cup anemometers on meteorological towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability; however, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install meteorological towers at potential sites. As a result, remote-sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. Although lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount of uncertainty surrounding the measurement of turbulence using these devices. Errors in lidar turbulence estimates are caused by a variety of factors, including instrument noise, volume averaging, and variance contamination, in which the magnitude of these factors is highly dependent on measurement height and atmospheric stability. As turbulence has a large impact on wind power production, errors in turbulence measurements will translate into errors in wind power prediction. The impact of using lidars rather than cup anemometers for wind power prediction must be understood if lidars are to be considered a viable alternative to cup anemometers.In this poster, the sensitivity of power prediction error to typical lidar turbulence measurement errors is assessed. Turbulence estimates from a vertically profiling WINDCUBE v2 lidar are compared to high-resolution sonic anemometer measurements at field sites in Oklahoma and Colorado to determine the degree of lidar turbulence error that can be expected under different atmospheric conditions. These errors are then incorporated into a power prediction model to estimate the sensitivity of power prediction error to turbulence measurement error. Power prediction models, including the standard binning method and a random forest method, were developed using data from the aeroelastic simulator FAST for a 1.5 MW turbine. The impact of lidar turbulence error on the predicted power from these different models is examined to determine the degree of turbulence measurement accuracy needed for accurate power prediction.

@InProceedings{Newman2016,
Title = {Quantifying the Effect of Lidar Turbulence Error on Wind Power Prediction},
Author = {Jennifer F. Newman and Andrew Clifton},
Booktitle = {Poster presented at the American Meteorological Society (AMS) 96th Annual Meeting},
Year = {2016},
Abstract = {Currently, cup anemometers on meteorological towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability; however, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install meteorological towers at potential sites. As a result, remote-sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. Although lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount of uncertainty surrounding the measurement of turbulence using these devices. Errors in lidar turbulence estimates are caused by a variety of factors, including instrument noise, volume averaging, and variance contamination, in which the magnitude of these factors is highly dependent on measurement height and atmospheric stability. As turbulence has a large impact on wind power production, errors in turbulence measurements will translate into errors in wind power prediction. The impact of using lidars rather than cup anemometers for wind power prediction must be understood if lidars are to be considered a viable alternative to cup anemometers.In this poster, the sensitivity of power prediction error to typical lidar turbulence measurement errors is assessed. Turbulence estimates from a vertically profiling WINDCUBE v2 lidar are compared to high-resolution sonic anemometer measurements at field sites in Oklahoma and Colorado to determine the degree of lidar turbulence error that can be expected under different atmospheric conditions. These errors are then incorporated into a power prediction model to estimate the sensitivity of power prediction error to turbulence measurement error. Power prediction models, including the standard binning method and a random forest method, were developed using data from the aeroelastic simulator FAST for a 1.5 MW turbine. The impact of lidar turbulence error on the predicted power from these different models is examined to determine the degree of turbulence measurement accuracy needed for accurate power prediction.},
Url = {http://www.nrel.gov/docs/fy16osti/65472.pdf}
}

• L. Pauscher, N. Vasiljevic, D. Callies, G. Lea, J. Mann, T. Klaas, J. Hieronimus, J. Gottschall, A. Schwesig, M. Kühn, and M. Courtney, “An inter-comparison study of multi- and dbs lidar measurements in complex terrain,” Remote sensing, vol. 8, iss. 9, p. 782, 2016. doi:10.3390/rs8090782
Wind measurements using classical profiling lidars suffer from systematic measurement errors in complex terrain. Moreover, their ability to measure turbulence quantities is unsatisfactory for wind-energy applications. This paper presents results from a measurement campaign during which multiple WindScanners were focused on one point next to a reference mast in complex terrain. This multi-lidar (ML) technique is also compared to a profiling lidar using the Doppler beam swinging (DBS) method. First- and second-order statistics of the radial wind velocities from the individual instruments and the horizontal wind components of several ML combinations are analysed in comparison to sonic anemometry and DBS measurements. The results for the wind speed show significantly reduced scatter and directional error for the ML method in comparison to the DBS lidar. The analysis of the second-order statistics also reveals a significantly better correlation for the ML technique than for the DBS lidar, when compared to the sonic. However, the probe volume averaging of the lidars leads to an attenuation of the turbulence at high wave numbers. Also the configuration (i.e., angles) of the WindScanners in the ML method seems to be more important for turbulence measurements. In summary, the results clearly show the advantages of the ML technique in complex terrain and indicate that it has the potential to achieve significantly higher accuracy in measuring turbulence quantities for wind-energy applications than classical profiling lidars.

@Article{Pauscher2016,
Title = {An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain},
Author = {Pauscher, Lukas and Vasiljevic, Nikola and Callies, Doron and Lea, Guillaume and Mann, Jakob and Klaas, Tobias and Hieronimus, Julian and Gottschall, Julia and Schwesig, Annedore and Kühn, Martin and Courtney, Michael},
Journal = {Remote Sensing},
Year = {2016},
Number = {9},
Pages = {782},
Volume = {8},
Abstract = {Wind measurements using classical profiling lidars suffer from systematic measurement errors in complex terrain. Moreover, their ability to measure turbulence quantities is unsatisfactory for wind-energy applications. This paper presents results from a measurement campaign during which multiple WindScanners were focused on one point next to a reference mast in complex terrain. This multi-lidar (ML) technique is also compared to a profiling lidar using the Doppler beam swinging (DBS) method. First- and second-order statistics of the radial wind velocities from the individual instruments and the horizontal wind components of several ML combinations are analysed in comparison to sonic anemometry and DBS measurements. The results for the wind speed show significantly reduced scatter and directional error for the ML method in comparison to the DBS lidar. The analysis of the second-order statistics also reveals a significantly better correlation for the ML technique than for the DBS lidar, when compared to the sonic. However, the probe volume averaging of the lidars leads to an attenuation of the turbulence at high wave numbers. Also the configuration (i.e., angles) of the WindScanners in the ML method seems to be more important for turbulence measurements. In summary, the results clearly show the advantages of the ML technique in complex terrain and indicate that it has the potential to achieve significantly higher accuracy in measuring turbulence quantities for wind-energy applications than classical profiling lidars.},
Doi = {10.3390/rs8090782},
ISSN = {2072-4292},
Url = {http://www.mdpi.com/2072-4292/8/9/782}
}

• A. Peña, A. Bechmann, D. Conti, and N. Angelou, “The fence experiment — full-scale lidar-based shelter observations,” Wind energy science, vol. 1, iss. 2, pp. 101-114, 2016. doi:10.5194/wes-1-101-2016
We present shelter measurements of a fence from a field experiment in Denmark. The measurements were performed with three lidars scanning on a vertical plane downwind of the fence. Inflow conditions are based on sonic anemometer observations of a nearby mast. For fence-undisturbed conditions, the lidars’ measurements agree well with those from the sonic anemometers and, at the mast position, the average inflow conditions are well described by the logarithmic profile. Seven cases are defined based on the relative wind direction to the fence, the fence porosity, and the inflow conditions. The larger the relative direction, the lower the effect of the shelter. For the case with the largest relative directions, no sheltering effect is observed in the far wake (distances ⪆ 6 fence heights downwind of the fence). When comparing a near-neutral to a stable case, a stronger shelter effect is noticed. The shelter is highest below  ≈ 1.46 fence heights and can sometimes be observed at all downwind positions (up to 11 fence heights downwind). Below the fence height, the porous fence has a lower impact on the flow close to the fence compared to the solid fence. Velocity profiles in the far wake converge onto each other using the self-preserving forms from two-dimensional wake analysis.

@Article{Pena2016,
Title = {The fence experiment -- full-scale lidar-based shelter observations},
Author = {Peña, A. and Bechmann, A. and Conti, D. and Angelou, N.},
Journal = {Wind Energy Science},
Year = {2016},
Number = {2},
Pages = {101--114},
Volume = {1},
Abstract = {We present shelter measurements of a fence from a field experiment in Denmark. The measurements were performed with three lidars scanning on a vertical plane downwind of the fence. Inflow conditions are based on sonic anemometer observations of a nearby mast. For fence-undisturbed conditions, the lidars' measurements agree well with those from the sonic anemometers and, at the mast position, the average inflow conditions are well described by the logarithmic profile. Seven cases are defined based on the relative wind direction to the fence, the fence porosity, and the inflow conditions. The larger the relative direction, the lower the effect of the shelter. For the case with the largest relative directions, no sheltering effect is observed in the far wake (distances ⪆ 6 fence heights downwind of the fence). When comparing a near-neutral to a stable case, a stronger shelter effect is noticed. The shelter is highest below  ≈ 1.46 fence heights and can sometimes be observed at all downwind positions (up to 11 fence heights downwind). Below the fence height, the porous fence has a lower impact on the flow close to the fence compared to the solid fence. Velocity profiles in the far wake converge onto each other using the self-preserving forms from two-dimensional wake analysis.},
Doi = {10.5194/wes-1-101-2016},
Url = {http://www.wind-energ-sci.net/1/101/2016/}
}

• S. Raach, D. Schlipf, F. Borisade, and P. W. Cheng, “Wake redirecting using feedback control to improve the power output of wind farms,” in Proceedings of the american control conference, Boston, MA, USA, 2016. doi:10.1109/ACC.2016.7525111
In future, a wind turbine will not only be seen as a single systems operating independently, but also as a component of a larger interacting system, the wind farm. To increase the efficiency of a wind farm, two main concepts have been proposed: axial induction control and wake redirecting. This contribution focuses on the latter. Remote sensing technologies in wind energy applications have opened new ways to control wind turbines. In this contribution, a further step is taken by using a remote sensing device for redirecting the wake of a wind turbine. A controller is proposed which uses the yaw actuator of a wind turbine to steer the wake center of the turbine to a desired position. The wake propagation from the wind turbine to the measurement location is modeled with a time delay. This forms a challenging problem for controller design. The controller follows the idea of the internal model principle and uses a model to predict the system behavior avoiding an overestimation of the error. Further, an adaptive filter is proposed in order to filter uncontrollable frequencies from the wake center estimation. The estimation from lidar measurement data is assumed to be perfect. Closed-loop simulations are conducted using the nominal system and a wind farm simulation tool, which was adapted to the scenario. The results are compared to the uncontrolled baseline case and a statically applied yaw offset. They show an increase in the total power output of the wind farm. Together with wake tracking methods, the approach can be considered as a promising step towards closed-loop wind farm control.

@InProceedings{Raach2016,
Title = {Wake redirecting using feedback control to improve the power output of wind farms},
Author = {S. Raach and D. Schlipf and F. Borisade and P. W. Cheng},
Booktitle = {Proceedings of the American Control Conference},
Year = {2016},
Abstract = {In future, a wind turbine will not only be seen as a single systems operating independently, but also as a component of a larger interacting system, the wind farm. To increase the efficiency of a wind farm, two main concepts have been proposed: axial induction control and wake redirecting. This contribution focuses on the latter. Remote sensing technologies in wind energy applications have opened new ways to control wind turbines. In this contribution, a further step is taken by using a remote sensing device for redirecting the wake of a wind turbine. A controller is proposed which uses the yaw actuator of a wind turbine to steer the wake center of the turbine to a desired position. The wake propagation from the wind turbine to the measurement location is modeled with a time delay. This forms a challenging problem for controller design. The controller follows the idea of the internal model principle and uses a model to predict the system behavior avoiding an overestimation of the error. Further, an adaptive filter is proposed in order to filter uncontrollable frequencies from the wake center estimation. The estimation from lidar measurement data is assumed to be perfect. Closed-loop simulations are conducted using the nominal system and a wind farm simulation tool, which was adapted to the scenario. The results are compared to the uncontrolled baseline case and a statically applied yaw offset. They show an increase in the total power output of the wind farm. Together with wake tracking methods, the approach can be considered as a promising step towards closed-loop wind farm control.},
Doi = {10.1109/ACC.2016.7525111},
Url = {http://dx.doi.org/10.1109/ACC.2016.7525111}
}

• D. Schlipf, “Prospects of multivariable feedforward control of wind turbines using lidar,” in Proceedings of the american control conference, Boston, MA, USA, 2016. doi:10.1109/ACC.2016.7525112
Current advances in lidar-technology provide the possibility of including wind preview information in the control design. Lidar-assisted collective pitch control is a simple, but promising approach to reduce the rotor speed variation and structural loads for full load operation. This work extends this approach to the transition between partial and full load operations. A multivariable controller is presented, which provides a simple update for the generator torque rate and the minimum pitch angle based on a nonlinear system inversion. The feedforward signals of the generator torque rate and the minimum pitch angle can be combined with conventional feedback controllers and the collective pitch feedforward controller for full load operation. This facilitates the modular application on commercial wind turbines. Simulations with a full aero-elastic wind turbine model and a lidar simulator show improved rotor speed regulation and significant reduction of tower loads, while only slightly decreasing power. Further, possibilities to transform the load reduction into energy increase are outlined.

@InProceedings{Schlipf2016b,
Title = {Prospects of multivariable feedforward control of wind turbines using lidar},
Author = {D. Schlipf},
Booktitle = {Proceedings of the American Control Conference},
Year = {2016},
Abstract = {Current advances in lidar-technology provide the possibility of including wind preview information in the control design. Lidar-assisted collective pitch control is a simple, but promising approach to reduce the rotor speed variation and structural loads for full load operation. This work extends this approach to the transition between partial and full load operations. A multivariable controller is presented, which provides a simple update for the generator torque rate and the minimum pitch angle based on a nonlinear system inversion. The feedforward signals of the generator torque rate and the minimum pitch angle can be combined with conventional feedback controllers and the collective pitch feedforward controller for full load operation. This facilitates the modular application on commercial wind turbines. Simulations with a full aero-elastic wind turbine model and a lidar simulator show improved rotor speed regulation and significant reduction of tower loads, while only slightly decreasing power. Further, possibilities to transform the load reduction into energy increase are outlined.},
Doi = {10.1109/ACC.2016.7525112},
Url = {http://dx.doi.org/10.1109/ACC.2016.7525112}
}

• D. Schlipf, “Lidar-assisted control concepts for wind turbines,” PhD Thesis, 2016. doi:10.18419/opus-8796
Current advances in lidar technology provide opportunities to take a fresh look at wind turbine control. The wind is not only the main energy source but also the major disturbance to the control system. Thus, knowledge of the incoming wind is valuable information for optimizing energy production and reducing structural loads. Due to the measurement principles and the complexity of the wind, the disturbance cannot be measured perfectly. This forms a challenging task for estimation and control. Within this thesis project, research has been carried out in the field of predictive control for onshore wind turbines. The thesis presents concepts of lidar-assisted control to reduce the structural loads and also to increase the energy yield of wind turbines, both of which make wind energy more competitive. The key challenges have not only been to develop appropriate feedforward control methods applicable to an industrial feedback controller, but also to investigate turbulence characteristics and to derive lidar measurements techniques to provide a usable preview signal. The combination of these findings made a proof-of-concept possible on two research wind turbines using a commercial and an adapted lidar system.

@PhdThesis{Schlipf2016,
Title = {Lidar-Assisted Control Concepts for Wind Turbines},
Author = {David Schlipf},
School = {University of Stuttgart},
Year = {2016},
Abstract = {Current advances in lidar technology provide opportunities to take a fresh look at wind turbine control. The wind is not only the main energy source but also the major disturbance to the control system. Thus, knowledge of the incoming wind is valuable information for optimizing energy production and reducing structural loads. Due to the measurement principles and the complexity of the wind, the disturbance cannot be measured perfectly. This forms a challenging task for estimation and control.
Within this thesis project, research has been carried out in the field of predictive control for onshore wind turbines. The thesis presents concepts of lidar-assisted control to reduce the structural loads and also to increase the energy yield of wind turbines, both of which make wind energy more competitive. The key challenges have not only been to develop appropriate feedforward control methods applicable to an industrial feedback controller, but also to investigate turbulence characteristics and to derive lidar measurements techniques to provide a usable preview signal. The combination of these findings made a proof-of-concept possible on two research wind turbines using a commercial and an adapted lidar system.},
Doi = {10.18419/opus-8796},
Url = {http://dx.doi.org/10.18419/opus-8796}
}

• M. Schmidt, J. J. Trujillo, and M. Kühn, “Orientation correction of wind direction measurements by means of staring lidar,” Journal of physics: conference series, vol. 749, iss. 1, p. 12005, 2016. doi:10.1088/1742-6596/749/1/012005
In spite of the efforts made at the time of installation of wind vanes or ultrasonic anemometers (Sonic), there is always a remaining uncertainty of several degrees in the absolute north of such sensors. In this research a method is presented to reduce the azimuthal orientation error of wind direction sensors by means of Doppler Lidar measurements. The method is based on the comparison between the conventional sensor and a distant long range lidar pointing to it in staring mode. By comparing their line-of-sight wind speeds any misalignment between both systems can be estimated more accurately. This method was applied in an measurement campaign in the offshore wind farm alpha ventus next to the meteorological mast FINO 1. The maximum alignment error of a Sonic was reduced to below ±1°. This accurate alignment has asserted, that no bias exists between Lidar and Sonic wind speed measurements.

@Article{Schmidt2016,
Title = {Orientation correction of wind direction measurements by means of staring lidar},
Author = {Michael Schmidt and Juan José Trujillo and Martin Kühn},
Journal = {Journal of Physics: Conference Series},
Year = {2016},
Number = {1},
Pages = {012005},
Volume = {749},
Abstract = {In spite of the efforts made at the time of installation of wind vanes or ultrasonic anemometers (Sonic), there is always a remaining uncertainty of several degrees in the absolute north of such sensors. In this research a method is presented to reduce the azimuthal orientation error of wind direction sensors by means of Doppler Lidar measurements. The method is based on the comparison between the conventional sensor and a distant long range lidar pointing to it in staring mode. By comparing their line-of-sight wind speeds any misalignment between both systems can be estimated more accurately. This method was applied in an measurement campaign in the offshore wind farm alpha ventus next to the meteorological mast FINO 1. The maximum alignment error of a Sonic was reduced to below ±1°. This accurate alignment has asserted, that no bias exists between Lidar and Sonic wind speed measurements.},
Doi = {10.1088/1742-6596/749/1/012005},
Url = {https://doi.org/10.1088/1742-6596/749/1/012005}
}

• A. Scholbrock, P. Fleming, D. Schlipf, A. Wright, K. Johnson, and N. Wang, “Lidar-enhanced wind turbine control: past, present, and future,” in Proceedings of the american control conference, Boston, MA, USA, 2016. doi:10.1109/ACC.2016.7525113
The main challenges in harvesting energy from the wind arise from the unknown incoming turbulent wind field. Balancing the competing interests of reduction in structural loads and increasing energy production is the goal of a wind turbine controller to reduce the cost of producing wind energy. Conventional wind turbines use feedback methods to optimize these goals, reacting to wind disturbances after they have already impacted the wind turbine. Lidar sensors offer a means to provide additional inputs to a wind turbine controller, enabling new techniques to improve control methods, allowing a controller to actuate a wind turbine in anticipation of an incoming wind disturbance. This paper will look at the development of lidar-enhanced controls and how they have been used for various turbine load reductions with pitch actuation, as well as increased energy production with improved yaw control. Ongoing work will also be discussed to show that combining pitch and torque control using feedforward nonlinear model predictive control can lead to both reduced loads and increased energy production. Future work is also proposed on extending individual wind turbine controls to the wind plant level and determining how lidars can be used for control methods to further lower the cost of wind energy by minimizing wake impacts in a wind farm.

@InProceedings{Scholbrock2016,
Title = {Lidar-enhanced wind turbine control: Past, present, and future},
Author = {A. Scholbrock and P. Fleming and D. Schlipf and A. Wright and K. Johnson and N. Wang},
Booktitle = {Proceedings of the American Control Conference},
Year = {2016},
Abstract = {The main challenges in harvesting energy from the wind arise from the unknown incoming turbulent wind field. Balancing the competing interests of reduction in structural loads and increasing energy production is the goal of a wind turbine controller to reduce the cost of producing wind energy. Conventional wind turbines use feedback methods to optimize these goals, reacting to wind disturbances after they have already impacted the wind turbine. Lidar sensors offer a means to provide additional inputs to a wind turbine controller, enabling new techniques to improve control methods, allowing a controller to actuate a wind turbine in anticipation of an incoming wind disturbance. This paper will look at the development of lidar-enhanced controls and how they have been used for various turbine load reductions with pitch actuation, as well as increased energy production with improved yaw control. Ongoing work will also be discussed to show that combining pitch and torque control using feedforward nonlinear model predictive control can lead to both reduced loads and increased energy production. Future work is also proposed on extending individual wind turbine controls to the wind plant level and determining how lidars can be used for control methods to further lower the cost of wind energy by minimizing wake impacts in a wind farm.},
Doi = {10.1109/ACC.2016.7525113},
Url = {http://dx.doi.org/10.1109/ACC.2016.7525113}
}

• E. Simley, N. Angelou, T. Mikkelsen, M. Sjöholm, J. Mann, and L. Y. Pao, “Characterization of wind velocities in the upstream induction zone of a wind turbine using scanning continuous-wave lidars,” Journal of renewable and sustainable energy, vol. 8, iss. 1, 2016. doi:10.1063/1.4940025
As a wind turbine generates power, induced velocities, lower than the freestream velocity, will be present upstream of the turbine due to perturbation of the flow by the rotor. In this study, the upstream induction zone of a 225 kW horizontal axis Vestas V27 wind turbine located at the Danish Technical University’s Risø campus is investigated using a scanning Light Detection and Ranging(lidar) system. Three short-range continuous-wave “WindScanner” lidars are positioned in the field around the V27 turbine allowing detection of all three components of the wind velocity vectors within the induction zone. The time-averaged mean wind speeds at different locations in the upstream induction zone are measured by scanning a horizontal plane at hub height and a vertical plane centered at the middle of the rotor extending roughly 1.5 rotor diameters (D) upstream of the rotor. Turbulence statistics in the induction zone are studied by more rapidly scanning along individual lines perpendicular to the rotor at different radial distances from the hub. The mean velocity measurements reveal that the longitudinal velocity reductions become greater closer to the rotor plane and closer to the center of the rotor. Velocity deficits of 1%–3% of the freestream value were observed 1 D upstream of the rotor, increasing at the rotor plane to 7.4% near the edge of the rotor and 18% near the center of the rotor while the turbine was operating with a high estimated mechanical coefficient of power (CP) of 0.56 yielding an estimated axial induction factor of 0.25. The velocity reductions relative to the freestream velocity become smaller when the turbine’s coefficient of power decreases; for a low CP of 0.16 resulting in an estimated induction factor of 0.04, the velocity deficits are ∼1% of the freestream value 1 D upstream of the rotor and only 6% at the rotor plane near the center of the rotor. Additionally, the mean radial wind speeds were found to increase close to the edge of the rotor disk indicating an expansion of the incoming flow around the rotor. Radial velocity magnitudes at the edge of the rotor disk of approximately 9% and 3% of the freestream longitudinal wind speed were measured for the abovementioned high and low CP values, respectively. Turbulence statistics, calculated using 2.5-min time series, suggest that the standard deviation of the longitudinal wind component decreases close to the rotor, while the standard deviation of the radial wind component appears to increase. When the turbine was operating with a high CP of 0.54 resulting in an estimated induction factor of 0.22, standard deviation decreases of up to 22% of the estimated freestream value and increases of up to 46% were observed for the longitudinal and radial components, respectively, near the center of the rotor.

@Article{Simley2016,
Title = {Characterization of wind velocities in the upstream induction zone of a wind turbine using scanning continuous-wave lidars},
Author = {Simley, Eric and Angelou, Nikolas and Mikkelsen, Torben and Sjöholm, Mikael and Mann, Jakob and Pao, Lucy Y.},
Journal = {Journal of Renewable and Sustainable Energy},
Year = {2016},
Number = {1},
Volume = {8},
Abstract = {As a wind turbine generates power, induced velocities, lower than the freestream velocity, will be present upstream of the turbine due to perturbation of the flow by the rotor. In this study, the upstream induction zone of a 225 kW horizontal axis Vestas V27 wind turbine located at the Danish Technical University's Risø campus is investigated using a scanning Light Detection and Ranging(lidar) system. Three short-range continuous-wave “WindScanner” lidars are positioned in the field around the V27 turbine allowing detection of all three components of the wind velocity vectors within the induction zone. The time-averaged mean wind speeds at different locations in the upstream induction zone are measured by scanning a horizontal plane at hub height and a vertical plane centered at the middle of the rotor extending roughly 1.5 rotor diameters (D) upstream of the rotor. Turbulence statistics in the induction zone are studied by more rapidly scanning along individual lines perpendicular to the rotor at different radial distances from the hub. The mean velocity measurements reveal that the longitudinal velocity reductions become greater closer to the rotor plane and closer to the center of the rotor. Velocity deficits of 1%–3% of the freestream value were observed 1 D upstream of the rotor, increasing at the rotor plane to 7.4% near the edge of the rotor and 18% near the center of the rotor while the turbine was operating with a high estimated mechanical coefficient of power (CP) of 0.56 yielding an estimated axial induction factor of 0.25. The velocity reductions relative to the freestream velocity become smaller when the turbine's coefficient of power decreases; for a low CP of 0.16 resulting in an estimated induction factor of 0.04, the velocity deficits are ∼1% of the freestream value 1 D upstream of the rotor and only 6% at the rotor plane near the center of the rotor. Additionally, the mean radial wind speeds were found to increase close to the edge of the rotor disk indicating an expansion of the incoming flow around the rotor. Radial velocity magnitudes at the edge of the rotor disk of approximately 9% and 3% of the freestream longitudinal wind speed were measured for the abovementioned high and low CP values, respectively. Turbulence statistics, calculated using 2.5-min time series, suggest that the standard deviation of the longitudinal wind component decreases close to the rotor, while the standard deviation of the radial wind component appears to increase. When the turbine was operating with a high CP of 0.54 resulting in an estimated induction factor of 0.22, standard deviation decreases of up to 22% of the estimated freestream value and increases of up to 46% were observed for the longitudinal and radial components, respectively, near the center of the rotor.},
Doi = {10.1063/1.4940025},
Url = {http://dx.doi.org/10.1063/1.4940025}
}

• P. Towers and L. B. Jones, “Real-time wind field reconstruction from lidar measurements using a dynamic wind model and state estimation,” Wind energy, vol. 19, iss. 1, pp. 133-150, 2016. doi:10.1002/we.1824
The use of light detection and ranging (LiDAR) instruments offer many potential benefits to the wind energy industry. Although much effort has been invested in developing such instruments, the fact remains that they provide limited spatio-temporal velocity measurements of the wind field. Moreover, LiDAR measurements only provide the radial (line-of-sight) velocity component of the wind, making it difficult to precisely determine wind magnitude and direction, owing to the so-called ‘cyclops’ dilemma. Motivated by a desire to extract more information from typical LiDAR data, this paper aims to show that it is possible to accurately estimate, in a real-time fashion, the radial and tangential velocity components of the wind field. We show how such reconstructions can be generated through the synthesis of an unscented Kalman filter that employs a low-order dynamic model of the wind to estimate the unmeasured velocities within the wind field, using repeated measurement updates from typical nacelle-mounted LiDAR instruments. This approach is validated upon synthetic data generated from large eddy simulations of the atmospheric boundary layer. The accuracy of the wind field estimates are validated across a variety of beam configurations, look directions, atmospheric stabilities and imperfect measurement conditions. The main outcome of this paper is a technique that offers the potential to accurately reconstruct wind fields from LiDAR data, overcoming the cyclops dilemma in the process. The ultimate aim of this research is to provide reliable gust detection warning systems to offshore construction workers, in addition to accurate wind field estimates for use in preview turbine pitch control systems.

@Article{Towers2016,
Title = {Real-time wind field reconstruction from LiDAR measurements using a dynamic wind model and state estimation},
Author = {Towers, P. and Jones, B. Ll.},
Journal = {Wind Energy},
Year = {2016},
Number = {1},
Pages = {133--150},
Volume = {19},
Abstract = {The use of light detection and ranging (LiDAR) instruments offer many potential benefits to the wind energy industry. Although much effort has been invested in developing such instruments, the fact remains that they provide limited spatio-temporal velocity measurements of the wind field. Moreover, LiDAR measurements only provide the radial (line-of-sight) velocity component of the wind, making it difficult to precisely determine wind magnitude and direction, owing to the so-called ‘cyclops’ dilemma. Motivated by a desire to extract more information from typical LiDAR data, this paper aims to show that it is possible to accurately estimate, in a real-time fashion, the radial and tangential velocity components of the wind field. We show how such reconstructions can be generated through the synthesis of an unscented Kalman filter that employs a low-order dynamic model of the wind to estimate the unmeasured velocities within the wind field, using repeated measurement updates from typical nacelle-mounted LiDAR instruments. This approach is validated upon synthetic data generated from large eddy simulations of the atmospheric boundary layer. The accuracy of the wind field estimates are validated across a variety of beam configurations, look directions, atmospheric stabilities and imperfect measurement conditions. The main outcome of this paper is a technique that offers the potential to accurately reconstruct wind fields from LiDAR data, overcoming the cyclops dilemma in the process. The ultimate aim of this research is to provide reliable gust detection warning systems to offshore construction workers, in addition to accurate wind field estimates for use in preview turbine pitch control systems.},
Doi = {10.1002/we.1824},
Url = {http://dx.doi.org/10.1002/we.1824}
}

• J. J. Trujillo, J. K. Seifert, I. Würth, D. Schlipf, and M. Kühn, “Full-field assessment of wind turbine near-wake deviation in relation to yaw misalignment,” Wind energy science, vol. 1, iss. 1, pp. 41-53, 2016. doi:10.5194/wes-1-41-2016
Presently there is a lack of data revealing the behaviour of the path followed by the near wake of full scale wind turbines and its dependence on yaw misalignment. Here we present an experimental analysis of the horizontal wake deviation of a 5 MW offshore wind turbine between 0.6 and 1.4 diameters downstream. The wake field has been scanned with a short-range lidar and the wake path has been reconstructed by means of two-dimensional Gaussian tracking. We analysed the measurements for rotor yaw misalignments arising in normal operation and during partial load, representing high thrust coefficient conditions. We classified distinctive wake paths with reference to yaw misalignment, based on the nacelle wind vane, in steps of 3° in a range of ±10.5°. All paths observed in the nacelle frame of reference showed a consistent convergence towards 0.9 rotor diameters downstream, suggesting a kind of wake deviation shift. This contrasts with published results from wind tunnels which in general report a convergence towards the rotor. The discrepancy is evidenced in particular in a comparison which we performed against published paths obtained by means of tip vortex tracking.

@Article{Trujillo2016,
Title = {Full-field assessment of wind turbine near-wake deviation in relation to yaw misalignment},
Author = {Trujillo, J. J. and Seifert, J. K. and Würth, I. and Schlipf, D. and Kühn, M.},
Journal = {Wind Energy Science},
Year = {2016},
Number = {1},
Pages = {41--53},
Volume = {1},
Abstract = {Presently there is a lack of data revealing the behaviour of the path followed by the near wake of full scale wind turbines and its dependence on yaw misalignment. Here we present an experimental analysis of the horizontal wake deviation of a 5 MW offshore wind turbine between 0.6 and 1.4 diameters downstream. The wake field has been scanned with a short-range lidar and the wake path has been reconstructed by means of two-dimensional Gaussian tracking. We analysed the measurements for rotor yaw misalignments arising in normal operation and during partial load, representing high thrust coefficient conditions. We classified distinctive wake paths with reference to yaw misalignment, based on the nacelle wind vane, in steps of 3° in a range of ±10.5°. All paths observed in the nacelle frame of reference showed a consistent convergence towards 0.9 rotor diameters downstream, suggesting a kind of wake deviation shift. This contrasts with published results from wind tunnels which in general report a convergence towards the rotor. The discrepancy is evidenced in particular in a comparison which we performed against published paths obtained by means of tip vortex tracking.},
Doi = {10.5194/wes-1-41-2016},
Url = {http://dx.doi.org/10.5194/wes-1-41-2016}
}

• L. Vollmer, G. Steinfeld, D. Heinemann, and M. Kühn, “Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an les study,” Wind energy science, vol. 1, iss. 2, pp. 129-141, 2016. doi:10.5194/wes-1-129-2016
An intentional yaw misalignment of wind turbines is currently discussed as one possibility to increase the overall energy yield of wind farms. The idea behind this control is to decrease wake losses of downstream turbines by altering the wake trajectory of the controlled upwind turbines. For an application of such an operational control, precise knowledge about the inflow wind conditions, the magnitude of wake deflection by a yawed turbine and the propagation of the wake is crucial. The dependency of the wake deflection on the ambient wind conditions as well as the uncertainty of its trajectory are not sufficiently covered in current wind farm control models. In this study we analyze multiple sources that contribute to the uncertainty of the estimation of the wake deflection downstream of yawed wind turbines in different ambient wind conditions. We find that the wake shapes and the magnitude of deflection differ in the three evaluated atmospheric boundary layers of neutral, stable and unstable thermal stability. Uncertainty in the wake deflection estimation increases for smaller temporal averaging intervals. We also consider the choice of the method to define the wake center as a source of uncertainty as it modifies the result. The variance of the wake deflection estimation increases with decreasing atmospheric stability. Control of the wake position in a highly convective environment is therefore not recommended.

@Article{Vollmer2016,
Title = {Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study},
Author = {Vollmer, L. and Steinfeld, G. and Heinemann, D. and Kühn, M.},
Journal = {Wind Energy Science},
Year = {2016},
Number = {2},
Pages = {129--141},
Volume = {1},
Abstract = {An intentional yaw misalignment of wind turbines is currently discussed as one possibility to increase the overall energy yield of wind farms. The idea behind this control is to decrease wake losses of downstream turbines by altering the wake trajectory of the controlled upwind turbines. For an application of such an operational control, precise knowledge about the inflow wind conditions, the magnitude of wake deflection by a yawed turbine and the propagation of the wake is crucial. The dependency of the wake deflection on the ambient wind conditions as well as the uncertainty of its trajectory are not sufficiently covered in current wind farm control models. In this study we analyze multiple sources that contribute to the uncertainty of the estimation of the wake deflection downstream of yawed wind turbines in different ambient wind conditions. We find that the wake shapes and the magnitude of deflection differ in the three evaluated atmospheric boundary layers of neutral, stable and unstable thermal stability. Uncertainty in the wake deflection estimation increases for smaller temporal averaging intervals. We also consider the choice of the method to define the wake center as a source of uncertainty as it modifies the result. The variance of the wake deflection estimation increases with decreasing atmospheric stability. Control of the wake position in a highly convective environment is therefore not recommended.},
Doi = {10.5194/wes-1-129-2016},
Url = {http://www.wind-energ-sci.net/1/129/2016/}
}

### 2015

• D. Bastine, M. Wächter, J. Peinke, D. Trabucchi, and M. Kühn, “Characterizing wake turbulence with staring lidar measurements,” Journal of physics: conference series, vol. 625, iss. 1, p. 12006, 2015. doi:10.1088/1742-6596/625/1/012006
Lidar measurements in the German offshore wind farm Alpha Ventus were performed to investigate the turbulence characteristics of wind turbine wakes. In particular, we compare measurements of the free flow in the surroundings of the wind turbines with measurements in the inner region of a wake flow behind one turbine. Our results indicate that wind turbines modulate the turbulent structures of the flow on a wide range of scales. For the data of the wake flow, the power spectrum as well as the multifractal intermittency coefficient reveal features of homogeneous isotropic turbulence. Thus, we conjecture that on scales of the rotor a new turbulent cascade is initiated, which determines the features of the turbulent wake flow quite independently from the more complex wind flow in the surroundings of the turbine.

@Article{Bastine2015,
Title = {Characterizing Wake Turbulence with Staring Lidar Measurements},
Author = {D Bastine and M Wächter and J Peinke and D Trabucchi and M Kühn},
Journal = {Journal of Physics: Conference Series},
Year = {2015},
Number = {1},
Pages = {012006},
Volume = {625},
Abstract = {Lidar measurements in the German offshore wind farm Alpha Ventus were performed to investigate the turbulence characteristics of wind turbine wakes. In particular, we compare measurements of the free flow in the surroundings of the wind turbines with measurements in the inner region of a wake flow behind one turbine. Our results indicate that wind turbines modulate the turbulent structures of the flow on a wide range of scales. For the data of the wake flow, the power spectrum as well as the multifractal intermittency coefficient reveal features of homogeneous isotropic turbulence. Thus, we conjecture that on scales of the rotor a new turbulent cascade is initiated, which determines the features of the turbulent wake flow quite independently from the more complex wind flow in the surroundings of the turbine.},
Doi = {10.1088/1742-6596/625/1/012006},
Url = {http://dx.doi.org/10.1088/1742-6596/625/1/012006}
}

• A. Clifton, M. Boquet, E. B. D. Roziers, A. Westerhellweg, M. Hofsäß, T. Klaas, K. Vogstad, P. Clive, M. Harris, S. Wylie, E. Osler, B. Banta, A. Choukulkar, J. Lundquist, and M. Aitken, “Remote sensing of complex flows by doppler wind lidar: issues and preliminary recommendations,” NREL, NREL/TP-5000-64634, 2015.
Remote sensing of winds using lidar has become popular and useful in the wind energy industry. Extensive experience has been gained with using lidar for applications including land-based and offshore resource assessment, plant operations, and turbine control. Prepared by members of International Energy Agency Task 32, this report describes the state of the art in the use of Doppler wind lidar for resource assessment in complex flows. The report will be used as input for future recommended practices on this topic.

@TechReport{Clifton2015,
Title = {Remote Sensing of Complex Flows by Doppler Wind Lidar: Issues and Preliminary Recommendations},
Author = {Andrew. Clifton and Matthieu Boquet and Edward Burin Des Roziers and Annette Westerhellweg and Martin Hofsäß and Tobias Klaas and Klaus Vogstad and Peter Clive and Mike Harris and Scott Wylie and Evan Osler and Bob Banta and Aditya Choukulkar and Julie Lundquist and Matthew Aitken},
Institution = {NREL},
Year = {2015},
Number = {NREL/TP-5000-64634},
Abstract = {Remote sensing of winds using lidar has become popular and useful in the wind energy industry. Extensive experience has been gained with using lidar for applications including land-based and offshore resource assessment, plant operations, and turbine control. Prepared by members of International Energy Agency Task 32, this report describes the state of the art in the use of Doppler wind lidar for resource assessment in complex flows. The report will be used as input for future recommended practices on this topic.},
Url = {http://www.nrel.gov/docs/fy16osti/64634.pdf}
}

• H. Fürst, D. Schlipf, M. Iribas Latour, and P. W. Cheng, “Design and evaluation of a lidar-based feedforward controller for the INNWIND.EU 10 MW wind turbine,” in Proceedings of the european wind energy association annual event, Paris, France, 2015. doi:10.18419/opus-8418
For the development of the next generation of multi megawatt wind turbines, advanced control concepts are one of the major tasks. Reduction of fatigue and extreme loading could help to improve the overall design process and make plants more cost effective. This work deals with the application of the promising methodology of feedforward control using nacelle-based lidar sensor measurements on a 10 MW wind turbine concept. After lidar data processing has been described, the feedforward controller is designed such that disturbances from the changing wind speed to the generator speed are compensated by adding an update to the collective pitch rate signal of the normal feedback controller. The evaluation of the feedforward controller is done in two steps: Firstly, simulations using perfect lidar data measurements are applied to check the robustness of the controller against model uncertainties. After that, simulations with realistic lidar measurements are investigated. To improve control performance, the scanning configuration of the used lidar system is optimized. Over all it can be shown that lidar-assisted control leads to significant load reductions, especially in the full load region of the 10 MW turbine.

@InProceedings{Fuerst2015,
Title = {Design and Evaluation of a Lidar-Based Feedforward Controller for the {INNWIND.EU} 10 {MW} Wind Turbine},
Author = {Fürst, Holger and Schlipf, David and Iribas Latour, Mikel and Cheng, Po Wen},
Booktitle = {Proceedings of the European Wind Energy Association annual event},
Year = {2015},
Abstract = {For the development of the next generation of multi megawatt wind turbines, advanced control concepts are one of the major tasks. Reduction of fatigue and extreme loading could help to improve the overall design process and make plants more cost effective. This work deals with the application of the promising methodology of feedforward control using nacelle-based lidar sensor measurements on a 10 MW wind turbine concept. After lidar data processing has been described, the feedforward controller is designed such that disturbances from the changing wind speed to the generator speed are compensated by adding an update to the collective pitch rate signal of the normal feedback controller. The evaluation of the feedforward controller is done in two steps: Firstly, simulations using perfect lidar data measurements are applied to check the robustness of the controller against model uncertainties. After that, simulations with realistic lidar measurements are investigated. To improve control performance, the scanning configuration of the used lidar system is optimized. Over all it can be shown that lidar-assisted control leads to significant load reductions, especially in the full load region of the 10 MW turbine.},
Doi = {10.18419/opus-8418},
Url = {http://dx.doi.org/10.18419/opus-8418}
}

• F. Haizmann, D. Schlipf, S. Raach, A. Scholbrock, A. Wright, C. Slinger, J. Medley, M. Harris, E. Bossanyi, and P. W. Cheng, “Optimization of a feed-forward controller using a cw-lidar system on the cart3,” in Proceedings of the american control conference, Chicago, Illinois, USA, 2015, pp. 3715-3720. doi:10.1109/ACC.2015.7171907
This work presents results from a new field-testing campaign conducted on the three-bladed Controls Advanced Research Turbine (CART3) at the National Renewable Energy Laboratory in 2014. Tests were conducted using a commercially available, nacelle-mounted continuous-wave lidar system from ZephIR Lidar for the implementation of a lidar-based collective pitch feed-forward controller. During the campaign, the data processing of the lidar system was optimized for higher availability. Furthermore, the optimal scan distance was investigated for the CART3 by means of a spectra-based analytical model and found to match the lidar’s capabilities well. Throughout the campaign the predicted correlation between the lidar measurements and the turbine’s reaction was confirmed from the measured data. Additionally, the baseline feedback controller’s gains were tuned based on a simulation study that included the lidar system to achieve further load reductions. This led to some promising first results, which are presented at the end of this paper.

@InProceedings{Haizmann2015,
Title = {Optimization of a Feed-Forward Controller Using a CW-lidar System on the CART3},
Author = {F Haizmann and D Schlipf and S Raach and A Scholbrock and A Wright and C Slinger and J Medley and M Harris and E Bossanyi and P W Cheng},
Booktitle = {Proceedings of the American Control Conference},
Year = {2015},
Month = {July},
Pages = {3715-3720},
Abstract = {This work presents results from a new field-testing campaign conducted on the three-bladed Controls Advanced Research Turbine (CART3) at the National Renewable Energy Laboratory in 2014. Tests were conducted using a commercially available, nacelle-mounted continuous-wave lidar system from ZephIR Lidar for the implementation of a lidar-based collective pitch feed-forward controller. During the campaign, the data processing of the lidar system was optimized for higher availability. Furthermore, the optimal scan distance was investigated for the CART3 by means of a spectra-based analytical model and found to match the lidar's capabilities well. Throughout the campaign the predicted correlation between the lidar measurements and the turbine's reaction was confirmed from the measured data. Additionally, the baseline feedback controller's gains were tuned based on a simulation study that included the lidar system to achieve further load reductions. This led to some promising first results, which are presented at the end of this paper.},
Doi = {10.1109/ACC.2015.7171907},
Url = {http://dx.doi.org/10.1109/ACC.2015.7171907}
}

• F. Haizmann, David, and P. W. Cheng, “Correlation-model of rotor-effective wind shears and wind speed for lidar-based individual pitch control,” in Proceedings of the german wind energy conference, 2015. doi:10.18419/opus-3976
In this work the spectra based model of the correlation between lidar systems and wind turbines is extended from rotor-effective wind speed only, to rotor-effective wind speed and linear horizontal and vertical shear components. This is achieved by the incorporation of a model based wind field reconstruction method solving a set of linear equations with the least-squares method. The model allows to optimize a lidar system’s measurement configuration for a specific wind turbine a-priori by means of direct and fast spectra calculations. Furthermore, it allows to assess the filter parameters to be expected and needed for the application of lidar-assisted control. By extending the model to rotor-effective linear shears, the results can be used for lidar-assisted individual pitch control.

@InProceedings{Haizmann2015a,
Title = {Correlation-Model of Rotor-Effective Wind Shears and Wind Speed for LiDAR-based Individual Pitch Control},
Author = {Florian Haizmann and David and Po Wen Cheng},
Booktitle = {Proceedings of the German Wind Energy Conference},
Year = {2015},
Abstract = {In this work the spectra based model of the correlation between lidar systems and wind turbines is extended from rotor-effective wind speed only, to rotor-effective wind speed and linear horizontal and vertical shear components. This is achieved by the incorporation of a model based wind field reconstruction method solving a set of linear equations with the least-squares method. The model allows to optimize a lidar system’s measurement configuration for a specific wind turbine a-priori by means of direct and fast spectra calculations. Furthermore, it allows to assess the filter parameters to be expected and needed for the application of lidar-assisted control. By extending the model to rotor-effective linear shears, the results can be used for lidar-assisted individual pitch control.},
Doi = {10.18419/opus-3976},
Url = {http://dx.doi.org/10.18419/opus-3976}
}

• T. Klaas, L. Pauscher, and D. Callies, “Lidar-mast deviations in complex terrain and their simulation using cfd,” Meteorologische zeitschrift, vol. 24, iss. 6, pp. 591-603, 2015. doi:10.1127/metz/2015/0637
LiDARs (Light Detection and Ranging) are becoming important tools for wind resource assessments in all kinds of terrain. Compared to mast measurements, mobility and flexibility are their greatest benefits. However, care needs to be taken when setting up a measurement campaign. The influence of complex terrain on the wind leads to inhomogeneous flow. This can cause considerable errors in ground based mono-static LiDAR measurements due to their measurement principle and simplifying assumptions.Within this work, wind measurements from Fraunhofer IWES’s 200 m research mast in complex terrain at “Rödeser Berg” in Kassel, Germany, and a pulsed Doppler LiDAR (Leosphere windcube), located at the mast, are compared. The relative deviation between the measurements of the horizontal wind speed by the LiDAR and the mast (LiDAR-mast deviations) varies with wind direction and height. It ranges from about −4 % underestimation to +2.5 % overestimation by the LiDAR – for heights between 120 and 200 m. Two steady-state Reynolds-Averaged-Navier-Stokes (RANS) Computational Fluid Dynamics (CFD)-models and a model based on linearized Navier-Stokes Equations were used to estimate the LiDAR error from a flow simulation. Model results were evaluated depending on model parameterisation such as forest height and density. Given the right parameterisations – especially for the forest model – the CFD-models showed a good performance when compared to the observed LiDAR-mast deviations. These simulations can thus be used to correct the LiDAR error induced by the complex flow.To demonstrate variations of LiDAR errors due to choice of measurement location, one of the models was run to calculate the wind flow in an area of 2 × 2km2$2\times2\,\text{km}^{2}$ around the 200 m measurement mast. This allows the visualization of the estimated LiDAR errors to characterize measurement locations. Results showed the significant variation of measurement errors due to the location.

@Article{Klaas2015,
Title = {LiDAR-mast deviations in complex terrain and their simulation using CFD},
Author = {Klaas, Tobias and Pauscher, Lukas and Callies, Doron},
Journal = {Meteorologische Zeitschrift},
Year = {2015},
Month = {11},
Number = {6},
Pages = {591-603},
Volume = {24},
Abstract = {LiDARs (Light Detection and Ranging) are becoming important tools for wind resource assessments in all kinds of terrain. Compared to mast measurements, mobility and flexibility are their greatest benefits. However, care needs to be taken when setting up a measurement campaign. The influence of complex terrain on the wind leads to inhomogeneous flow. This can cause considerable errors in ground based mono-static LiDAR measurements due to their measurement principle and simplifying assumptions.Within this work, wind measurements from Fraunhofer IWES’s 200 m research mast in complex terrain at “Rödeser Berg” in Kassel, Germany, and a pulsed Doppler LiDAR (Leosphere windcube), located at the mast, are compared. The relative deviation between the measurements of the horizontal wind speed by the LiDAR and the mast (LiDAR-mast deviations) varies with wind direction and height. It ranges from about −4 % underestimation to +2.5 % overestimation by the LiDAR - for heights between 120 and 200 m. Two steady-state Reynolds-Averaged-Navier-Stokes (RANS) Computational Fluid Dynamics (CFD)-models and a model based on linearized Navier-Stokes Equations were used to estimate the LiDAR error from a flow simulation. Model results were evaluated depending on model parameterisation such as forest height and density. Given the right parameterisations – especially for the forest model – the CFD-models showed a good performance when compared to the observed LiDAR-mast deviations. These simulations can thus be used to correct the LiDAR error induced by the complex flow.To demonstrate variations of LiDAR errors due to choice of measurement location, one of the models was run to calculate the wind flow in an area of 2 × 2km2$2\times2\,\text{km}^{2}$ around the 200 m measurement mast. This allows the visualization of the estimated LiDAR errors to characterize measurement locations. Results showed the significant variation of measurement errors due to the location.},
Doi = {10.1127/metz/2015/0637},
Publisher = {Schweizerbart Science Publishers},
Url = {http://dx.doi.org/10.1127/metz/2015/0637}
}

• A. Kumar, E. A. Bossayni, A. K. Scholbrock, P. A. Fleming, M. Boquet, and R. Krishnamurthy, “Field testing of lidar assisted feedforward control algorithms for improved speed control and fatigue load reduction on a 600 kW wind turbine,” in Proceedings of the european wind energy association annual event, Paris, France, 2015.
A severe challenge in controlling wind turbines is ensuring controller performance in the presence of a stochastic and unknown wind field, relying on the response of the turbine to generate control actions. Recent technologies such as LIDAR, allow sensing of the wind field before it reaches the rotor. In this work a field-testing campaign to test LIDAR Assisted Control (LAC) has been undertaken on a 600-kW turbine using a fixed, five-beam LIDAR system. The campaign compared the performance of a baseline controller to four LACs with progressively lower levels of feedback using 35 hours of collected data. The collected data indicates that utilising measurements from multiple range gates on a pulsed LIDAR system can result in rotor averaged wind speed (RAWS) estimates with greater levels of correlation with wind speed at the rotor than using a single range gate. The LACs showed higher levels of speed control performance with significantly reduced levels of pitch activity and generally lower levels of tower excitation. Although the loading spectrum for the test turbine was dominated by responses at twice the rotor speed (2P) and the first tower fore-aft natural frequency, the reduction is likely to show greater relative significance on typical full-sized turbines, which show lower excitation levels due to harmonic clashes.

@InProceedings{Kumar2015,
Title = {Field Testing of LIDAR Assisted Feedforward Control Algorithms for Improved Speed Control and Fatigue Load Reduction on a 600 {kW} Wind Turbine},
Author = {A Kumar and E A Bossayni and A K Scholbrock and P A Fleming and M Boquet and R Krishnamurthy},
Booktitle = {Proceedings of the European Wind Energy Association annual event},
Year = {2015},
Month = {November},
Abstract = {A severe challenge in controlling wind turbines is ensuring controller performance in the presence of a stochastic and unknown wind field, relying on the response of the turbine to generate control actions. Recent technologies such as LIDAR, allow sensing of the wind field before it reaches the rotor.
In this work a field-testing campaign to test LIDAR Assisted Control (LAC) has been undertaken on a 600-kW turbine using a fixed, five-beam LIDAR system. The campaign compared the performance of a baseline controller to four LACs with progressively lower levels of feedback using 35 hours of collected data.
The collected data indicates that utilising measurements from multiple range gates on a pulsed LIDAR system can result in rotor averaged wind speed (RAWS) estimates with greater levels of correlation with wind speed at the rotor than using a single range gate. The LACs showed higher levels of speed control performance with significantly reduced levels of pitch activity and generally lower levels of tower excitation. Although the loading spectrum for the test turbine was dominated by responses at twice the rotor speed (2P) and the first tower fore-aft natural frequency, the reduction is likely to show greater relative significance on typical full-sized turbines, which show lower excitation levels due to harmonic clashes.},
Url = {http://www.nrel.gov/docs/fy16osti/65062.pdf}
}

• J. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton, “Quantifying error of lidar and sodar doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics,” Atmospheric measurement techniques, vol. 8, iss. 2, pp. 907-920, 2015. doi:10.5194/amt-8-907-2015
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s−1 at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s−1) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s−1 (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.

@Article{Lundquist2015,
Title = {Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics},
Author = {Lundquist, J. K. and Churchfield, M. J. and Lee, S. and Clifton, A.},
Journal = {Atmospheric Measurement Techniques},
Year = {2015},
Number = {2},
Pages = {907--920},
Volume = {8},
Abstract = {Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors.
To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s−1 at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s−1) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s−1 (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.},
Doi = {10.5194/amt-8-907-2015},
Url = {http://dx.doi.org/10.5194/amt-8-907-2015}
}

• E. Machefaux, G. C. Larsen, N. Troldborg, K. S. Hansen, N. Angelou, T. Mikkelsen, and J. Mann, “Investigation of wake interaction using full-scale lidar measurements and large eddy simulation,” Wind energy, 2015. doi:10.1002/we.1936
n this paper, wake interaction resulting from two stall regulated turbines aligned with the incoming wind is studied experimentally and numerically. The experimental work is based on a full-scale remote sensing campaign involving three nacelle mounted scanning lidars. A thorough analysis and interpretation of the measurements is performed to overcome either the lack of or the poor calibration of relevant turbine operational sensors, as well as other uncertainties inherent in resolving wakes from full-scale experiments. The numerical work is based on the in-house EllipSys3D computational fluid dynamics flow solver, using large eddy simulation and fully turbulent inflow. The rotors are modelled using the actuator disc technique. A mutual validation of the computational fluid dynamics model with the measurements is conducted for a selected dataset, where wake interaction occurs. This validation is based on a comparison between wake deficit, wake generated turbulence, turbine power production and thrust force. An excellent agreement between measurement and simulation is seen in both the fixed and the meandering frame of reference.

@Article{Machefaux2015a,
Title = {Investigation of wake interaction using full-scale lidar measurements and large eddy simulation},
Author = {Machefaux, E. and Larsen, G. C. and Troldborg, N. and Hansen, Kurt. S. and Angelou, N. and Mikkelsen, T. and Mann, J.},
Journal = {Wind Energy},
Year = {2015},
Abstract = {n this paper, wake interaction resulting from two stall regulated turbines aligned with the incoming wind is studied experimentally and numerically. The experimental work is based on a full-scale remote sensing campaign involving three nacelle mounted scanning lidars. A thorough analysis and interpretation of the measurements is performed to overcome either the lack of or the poor calibration of relevant turbine operational sensors, as well as other uncertainties inherent in resolving wakes from full-scale experiments. The numerical work is based on the in-house EllipSys3D computational fluid dynamics flow solver, using large eddy simulation and fully turbulent inflow. The rotors are modelled using the actuator disc technique. A mutual validation of the computational fluid dynamics model with the measurements is conducted for a selected dataset, where wake interaction occurs. This validation is based on a comparison between wake deficit, wake generated turbulence, turbine power production and thrust force. An excellent agreement between measurement and simulation is seen in both the fixed and the meandering frame of reference.},
Doi = {10.1002/we.1936},
Url = {http://dx.doi.org/10.1002/we.1936}
}

• E. Machefaux, G. C. Larsen, N. Troldborg, M. Gaunaa, and A. Rettenmeier, “Empirical modeling of single-wake advection and expansion using full-scale pulsed lidar-based measurements,” Wind energy, vol. 18, iss. 12, pp. 2085-2103, 2015. doi:10.1002/we.1805
In the present paper, single-wake dynamics have been studied both experimentally and numerically. The use of pulsed lidar measurements allows for validation of basic dynamic wake meandering modeling assumptions. Wake center tracking is used to estimate the wake advection velocity experimentally and to obtain an estimate of the wake expansion in a fixed frame of reference. A comparison shows good agreement between the measured average expansion and the Computational Fluid Dynamics (CFD) large eddy simulation–actuator line computations. Frandsen’s expansion model seems to predict the wake expansion fairly well in the far wake but lacks accuracy in the outer region of the near wake. An empirical relationship, relating maximum wake induction and wake advection velocity, is derived and linked to the characteristics of a spherical vortex structure. Furthermore, a new empirical model for single-wake expansion is proposed based on an initial wake expansion in the pressure-driven flow regime and a spatial gradient computed from the large-scale lateral velocities, and thus inspired by the basic assumption behind the dynamic wake meandering model.

@Article{Machefaux2015,
Title = {Empirical modeling of single-wake advection and expansion using full-scale pulsed lidar-based measurements},
Author = {Machefaux, E. and Larsen, G. C. and Troldborg, N. and Gaunaa, M. and Rettenmeier, A.},
Journal = {Wind Energy},
Year = {2015},
Number = {12},
Pages = {2085--2103},
Volume = {18},
Abstract = {In the present paper, single-wake dynamics have been studied both experimentally and numerically. The use of pulsed lidar measurements allows for validation of basic dynamic wake meandering modeling assumptions. Wake center tracking is used to estimate the wake advection velocity experimentally and to obtain an estimate of the wake expansion in a fixed frame of reference. A comparison shows good agreement between the measured average expansion and the Computational Fluid Dynamics (CFD) large eddy simulation–actuator line computations. Frandsen's expansion model seems to predict the wake expansion fairly well in the far wake but lacks accuracy in the outer region of the near wake. An empirical relationship, relating maximum wake induction and wake advection velocity, is derived and linked to the characteristics of a spherical vortex structure. Furthermore, a new empirical model for single-wake expansion is proposed based on an initial wake expansion in the pressure-driven flow regime and a spatial gradient computed from the large-scale lateral velocities, and thus inspired by the basic assumption behind the dynamic wake meandering model.},
Doi = {10.1002/we.1805},
Url = {http://dx.doi.org/10.1002/we.1805}
}

• R. K. Newsom, L. K. Berg, W. J. Shaw, and M. L. Fischer, “Turbine-scale wind field measurements using dual-Doppler lidar,” Wind energy, vol. 18, iss. 2, pp. 219-235, 2015. doi:10.1002/we.1691
Spatially resolved measurements of microscale winds are retrieved using scanning dual-Doppler lidar and then compared with independent in situ wind measurements. Data for this study were obtained during a month-long field campaign conducted at a site in north-central Oklahoma in November of 2010. Observational platforms include one instrumented 60 m meteorological tower and two scanning coherent Doppler lidars. The lidars were configured to perform coordinated dual-Doppler scans surrounding the 60 m tower, and the resulting radial velocity observations were processed to retrieve the three-component velocity vector field on surfaces defined by the intersecting scan planes. The dual-Doppler analysis method is described, and three-dimensional visualizations of the retrieved fields are presented. The retrieved winds are compared with sonic anemometer (SA) measurements at the 60 m level on the tower. The Pearson correlation coefficient between the retrievals and the SA wind speeds was greater than 0.97, and the wind direction difference was very small (<0.1o), suggesting that the dual-Doppler technique can be used to examine fine-scale variations in the flow. However, the mean percent difference between the SA and dual-Doppler wind speed was approximately 15%, with the SA consistently measuring larger wind speeds. To identify the source of the discrepancy, a multi-instrument intercomparison study was performed involving lidar wind speeds derived from standard velocity-azimuth display (VAD) analysis of plan position indicator scan data, a nearby 915 MHz radar wind profiler (RWP) and radiosondes. The lidar VAD, RWP and radiosondes wind speeds were found to agree to within 3%. By contrast, SA wind speeds were found to be approximately 14% larger than the lidar VAD wind speeds. These results suggest that the SA produced wind speeds that were too large.

@Article{Newsom2015,
Title = {{Turbine-scale wind field measurements using dual-Doppler lidar}},
Author = {R K. Newsom and L K. Berg and W J. Shaw and M L. Fischer},
Journal = {Wind Energy},
Year = {2015},
Number = {2},
Pages = {219-235},
Volume = {18},
Abstract = {Spatially resolved measurements of microscale winds are retrieved using scanning dual-Doppler lidar and then compared with independent in situ wind measurements. Data for this study were obtained during a month-long field campaign conducted at a site in north-central Oklahoma in November of 2010. Observational platforms include one instrumented 60 m meteorological tower and two scanning coherent Doppler lidars. The lidars were configured to perform coordinated dual-Doppler scans surrounding the 60 m tower, and the resulting radial velocity observations were processed to retrieve the three-component velocity vector field on surfaces defined by the intersecting scan planes. The dual-Doppler analysis method is described, and three-dimensional visualizations of the retrieved fields are presented.
The retrieved winds are compared with sonic anemometer (SA) measurements at the 60 m level on the tower. The Pearson correlation coefficient between the retrievals and the SA wind speeds was greater than 0.97, and the wind direction difference was very small (<0.1o), suggesting that the dual-Doppler technique can be used to examine fine-scale variations in the flow. However, the mean percent difference between the SA and dual-Doppler wind speed was approximately 15%, with the SA consistently measuring larger wind speeds. To identify the source of the discrepancy, a multi-instrument intercomparison study was performed involving lidar wind speeds derived from standard velocity-azimuth display (VAD) analysis of plan position indicator scan data, a nearby 915 MHz radar wind profiler (RWP) and radiosondes. The lidar VAD, RWP and radiosondes wind speeds were found to agree to within 3%. By contrast, SA wind speeds were found to be approximately 14% larger than the lidar VAD wind speeds. These results suggest that the SA produced wind speeds that were too large.},
Doi = {10.1002/we.1691},
Url = {http://dx.doi.org/10.1002/we.1691}
}

• A. Sathe, J. Mann, N. Vasiljevic, and G. Lea, “A six-beam method to measure turbulence statistics using ground-based wind lidars,” Atmospheric measurement techniques, vol. 8, iss. 2, pp. 729-740, 2015. doi:10.5194/amt-8-729-2015
A so-called six-beam method is proposed to measure atmospheric turbulence using a ground-based wind lidar. This method requires measurement of the radial velocity variances at five equally spaced azimuth angles on the base of a scanning cone and one measurement at the centre of the scanning circle, i.e.using a vertical beam at the same height. The scanning configuration is optimized to minimize the sum of the random errors in the measurement of the second-order moments of the components (u,v, w) of the wind field. We present this method as an alternative to the so-called velocity azimuth display (VAD) method that is routinely used in commercial wind lidars, and which usually results in significant averaging effects of measured turbulence. In the VAD method, the high frequency radial velocity measurements are used instead of their variances. The measurements are performed using a pulsed lidar (WindScanner), and the derived turbulence statistics (using both methods) such as the u and v variances are compared with those obtained from a reference cup anemometer and a wind vane at 89 m height under different atmospheric stabilities. The measurements show that in comparison to the reference cup anemometer, depending on the atmospheric stability and the wind field component, the six-beam method measures between 85 and 101% of the reference turbulence, whereas the VAD method measures between 66 and 87% of the reference turbulence.

@Article{Sathe2015,
Title = {{A six-beam method to measure turbulence statistics using ground-based wind lidars}},
Author = {A. Sathe and J. Mann and N. Vasiljevic and G. Lea},
Journal = {Atmospheric Measurement Techniques},
Year = {2015},
Number = {2},
Pages = {729-740},
Volume = {8},
Abstract = {A so-called six-beam method is proposed to measure atmospheric turbulence using a ground-based wind lidar. This method requires measurement of the radial velocity variances at five equally spaced azimuth angles on the base of a scanning cone and one measurement at the centre of the scanning circle, i.e.using a vertical beam at the same height. The scanning configuration is optimized to minimize the sum of the random errors in the measurement of the second-order moments of the components (u,v, w) of the wind field. We present this method as an alternative to the so-called velocity azimuth display (VAD) method that is routinely used in commercial wind lidars, and which usually results in significant averaging effects of measured turbulence. In the VAD method, the high frequency radial velocity measurements are used instead of their variances. The measurements are performed using a pulsed lidar (WindScanner), and the derived turbulence statistics (using both methods) such as the u and v variances are compared with those obtained from a reference cup anemometer and a wind vane at 89 m height under different atmospheric stabilities. The measurements show that in comparison to the reference cup anemometer, depending on the atmospheric stability and the wind field component, the six-beam method measures between 85 and 101% of the reference turbulence, whereas the VAD method measures between 66 and 87% of the reference turbulence.},
Doi = {10.5194/amt-8-729-2015},
Url = {http://dx.doi.org/10.5194/amt-8-729-2015}
}

• A. Sathe, R. Banta, L. Pauscher, K. Vogstad, D. Schlipf, and S. Wylie, “Estimating turbulence statistics and parameters from ground- and nacelle-based lidar measurements,” International Energy Agency, Technical Report , 2015.
The International Energy Agency Implementing Agreement for Co-operation in the Research, Development and Deployment of Wind Energy Systems (IEA Wind) is a vehicle for member countries to exchange information on the planning and execution of national, large-scale wind system projects and to undertake co-operative research and development projects called Tasks or Annexes. As a final result of research carried out in the IEA Wind Tasks, Recommended Practices, Best Practices, or Expert Group Reports may be issued. These documents have been developed and reviewed by experts in the specialized area they address. They have been reviewed and approved by participants in the research Task, and they have been reviewed and approved by the IEA Wind Executive Committee as guidelines useful in the development and deployment of wind energy systems. Use of these documents is completely voluntary. However, these documents are often adopted in part or in total by other standards-making bodies. A Recommended Practices document includes actions and procedures recommended by the experts involved in the research project. A Best Practices document includes suggested actions and procedures based on good industry practices collected during the research project. An Experts Group Studies report includes the latest background information on the topic as well as a survey of practices, where possible. Previously issued IEA Wind Recommended Practices, Best Practices, and Expert Group Reports can be found here on the Task 11 web pages.

@TechReport{Sathe2015b,
Title = {Estimating turbulence statistics and parameters from ground- and nacelle-based lidar measurements},
Author = {A. Sathe and R. Banta and L. Pauscher and K. Vogstad and D. Schlipf and S. Wylie},
Institution = {International Energy Agency},
Year = {2015},
Month = {October},
Type = {Technical Report},
Abstract = {The International Energy Agency Implementing Agreement for Co-operation in the Research, Development and Deployment of Wind Energy Systems (IEA Wind) is a vehicle for member countries to exchange information on the planning and execution of national, large-scale wind system projects and to undertake co-operative research and development projects called Tasks or Annexes. As a final result of research carried out in the IEA Wind Tasks, Recommended Practices, Best Practices, or Expert Group Reports may be issued. These documents have been developed and reviewed by experts in the specialized area they address. They have been reviewed and approved by participants in the research Task, and they have been reviewed and approved by the IEA Wind Executive Committee as guidelines useful in the development and deployment of wind energy systems. Use of these documents is completely voluntary. However, these documents are often adopted in part or in total by other standards-making bodies. A Recommended Practices document includes actions and procedures recommended by the experts involved in the research project. A Best Practices document includes suggested actions and procedures based on good industry practices collected during the research project. An Experts Group Studies report includes the latest background information on the topic as well as a survey of practices, where possible. Previously issued IEA Wind Recommended Practices, Best Practices, and Expert Group Reports can be found here on the Task 11 web pages.},
Source = {http://orbit.dtu.dk/en/publications/estimating-turbulence-statistics-and-parameters-from-ground-and-nacellebased-lidar-measurements(7cf8c041-4762-49b5-9ef4-575a490fea70).html},
Url = {http://orbit.dtu.dk/en/publications/estimating-turbulence-statistics-and-parameters-from-ground-and-nacellebased-lidar-measurements%287cf8c041-4762-49b5-9ef4-575a490fea70%29.html}
}

• D. Schlipf, E. Simley, F. Lemmer, L. Pao, and P. W. Cheng, “Collective pitch feedforward control of floating wind turbines using lidar,” Journal of ocean and wind energy, vol. 2, iss. 4, pp. 223-230, 2015. doi:10.17736/jowe.2015.arr04
In this work a collective pitch feedforward controller for floating wind turbines is presented. The feedforward controller provides a pitch rate update to a conventional feedback controller based on a wind speed preview. The controller is designed similar to the one for onshore turbines, which has proven its capability to improve wind turbine control performance in field tests. In a first design step, perfect wind preview and a calm sea is assumed. Under these assumptions the feedforward controller is able to compensate almost perfectly the effect of changing wind speed to the rotor speed of a full nonlinear model over the entire full load region. In a second step, a nacelle-based lidar is simulated scanning the same wind field which is used also for the aero-hydro-servo-elastic simulation. With model-based wind field reconstruction methods, the rotor effective wind speed is estimated from the raw lidar data and is used in the feedforward controller after filtering out the uncorrelated frequencies. Simulation results show that even with a more realistic wind preview, the feedforward controller is able to significantly reduce rotor speed and power variations. Furthermore, structural loads on the tower, rotor shaft, and blades are decreased. A comparison to a theoretical investigation shows that the reduction in rotor speed regulation is close to the optimum

@Article{Schlipf2015a,
Title = {Collective Pitch Feedforward Control of Floating Wind Turbines Using Lidar},
Author = {David Schlipf and Eric Simley and Frank Lemmer and Lucy Pao and Po Wen Cheng},
Journal = {Journal of Ocean and Wind Energy},
Year = {2015},
Number = {4},
Pages = {223-230},
Volume = {2},
Abstract = {In this work a collective pitch feedforward controller for floating wind turbines is presented. The feedforward controller provides a pitch rate update to a conventional feedback controller based on a wind speed preview. The controller is designed similar to the one for onshore turbines, which has proven its capability to improve wind turbine control performance in field tests. In a first design step, perfect wind preview and a calm sea is assumed. Under these assumptions the feedforward controller is able to compensate almost perfectly the effect of changing wind speed to the rotor speed of a full nonlinear model over the entire full load region. In a second step, a nacelle-based lidar is simulated scanning the same wind field which is used also for the aero-hydro-servo-elastic simulation. With model-based wind field reconstruction methods, the rotor effective wind speed is estimated from the raw lidar data and is used in the feedforward controller after filtering out the uncorrelated frequencies. Simulation results show that even with a more realistic wind preview, the feedforward controller is able to significantly reduce rotor speed and power variations. Furthermore, structural loads on the tower, rotor shaft, and blades are decreased. A comparison to a theoretical investigation shows that the reduction in rotor speed regulation is close to the optimum},
Doi = {10.17736/jowe.2015.arr04},
Url = {http://dx.doi.org/}
}

• D. Schlipf, P. Fleming, S. Raach, A. Scholbrock, F. Haizmann, R. Krishnamurthy, M. Boquet, A. Wright, and P. W. Cheng, “An adaptive data processing technique for lidar-assisted control to bridge the gap between lidar systems and wind turbines,” in Proceedings of the european wind energy association annual event, Paris, France, 2015. doi:10.18419/opus-8419
This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited or can even result in harmful control action. An online analysis of the lidar and turbine data is necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross-correlation to determine the time shift between both signals. Further, we present initial results from an ongoing campaign in which this system was employed for providing lidar preview for feedforward pitch control.

@InProceedings{Schlipf2015c,
Title = {An Adaptive Data Processing Technique for Lidar-Assisted Control to Bridge the Gap between Lidar Systems and Wind Turbines},
Author = {D Schlipf and P Fleming and S Raach and A Scholbrock and F Haizmann and R Krishnamurthy and M Boquet and A Wright and P W Cheng},
Booktitle = {Proceedings of the European Wind Energy Association annual event},
Year = {2015},
Abstract = {This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited or can even result in harmful control action. An online analysis of the lidar and turbine data is necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross-correlation to determine the time shift between both signals. Further, we present initial results from an ongoing campaign in which this system was employed for providing lidar preview for feedforward pitch control.},
Doi = {10.18419/opus-8419},
Url = {http://dx.doi.org/10.18419/opus-8419}
}

• D. Schlipf, F. Haizmann, N. Cosack, T. Siebers, and P. W. Cheng, “Detection of wind evolution and lidar trajectory optimization for lidar-assisted wind turbine control,” Meteorologische zeitschrift, vol. 24, iss. 6, pp. 565-579, 2015. doi:10.1127/metz/2015/0634
Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as lidar, the information about the incoming wind field – the main disturbance to the system – can be made available ahead of time. Initial field testing of collective pitch feedforward control shows, that lidar measurements are only beneficial if they are filtered properly to avoid harmful control action. However, commercial lidar systems developed for site assessment are usually unable to provide a usable signal for real time control. Recent research shows, that the correlation between the measurement of rotor effective wind speed and the turbine reaction can be modeled and that the model can be used to optimize a scan pattern. This correlation depends on several criteria such as turbine size, position of the measurements, measurement volume, and how the wind evolves on its way towards the rotor. In this work the longitudinal wind evolution is identified with the line-of-sight measurements of a pulsed lidar system installed on a large commercial wind turbine. This is done by staring directly into the inflowing wind during operation of the turbine and fitting the coherence between the wind at different measurement distances to an exponential model taking into account the yaw misalignment, limitation to line-of-sight measurements and the pulse volume. The identified wind evolution is then used to optimize the scan trajectory of a scanning lidar for lidar-assisted feedforward control in order to get the best correlation possible within the constraints of the system. Further, an adaptive filer is fitted to the modeled correlation to avoid negative impact of feedforward control because of uncorrelated frequencies of the wind measurement. The main results of the presented work are a first estimate of the wind evolution in front of operating wind turbines and an approach which manufacturers of lidar systems can use to improve their devices to better assist preview control concepts.

@Article{Schlipf2015b,
Title = {Detection of Wind Evolution and Lidar Trajectory Optimization for Lidar-Assisted Wind Turbine Control},
Author = {Schlipf, David and Haizmann, Florian and Cosack, Nicolai and Siebers, Tom and Cheng, Po Wen},
Journal = {Meteorologische Zeitschrift},
Year = {2015},
Number = {6},
Pages = {565-579},
Volume = {24},
Abstract = {Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as lidar, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Initial field testing of collective pitch feedforward control shows, that lidar measurements are only beneficial if they are filtered properly to avoid harmful control action. However, commercial lidar systems developed for site assessment are usually unable to provide a usable signal for real time control. Recent research shows, that the correlation between the measurement of rotor effective wind speed and the turbine reaction can be modeled and that the model can be used to optimize a scan pattern. This correlation depends on several criteria such as turbine size, position of the measurements, measurement volume, and how the wind evolves on its way towards the rotor. In this work the longitudinal wind evolution is identified with the line-of-sight measurements of a pulsed lidar system installed on a large commercial wind turbine. This is done by staring directly into the inflowing wind during operation of the turbine and fitting the coherence between the wind at different measurement distances to an exponential model taking into account the yaw misalignment, limitation to line-of-sight measurements and the pulse volume. The identified wind evolution is then used to optimize the scan trajectory of a scanning lidar for lidar-assisted feedforward control in order to get the best correlation possible within the constraints of the system. Further, an adaptive filer is fitted to the modeled correlation to avoid negative impact of feedforward control because of uncorrelated frequencies of the wind measurement. The main results of the presented work are a first estimate of the wind evolution in front of operating wind turbines and an approach which manufacturers of lidar systems can use to improve their devices to better assist preview control concepts.},
Doi = {10.1127/metz/2015/0634},
Url = {http://dx.doi.org/10.1127/metz/2015/0634}
}

• E. Simley, “Wind speed preview measurement and estimation for feedforward control of wind turbines,” PhD Thesis, 2015.
Wind turbines typically rely on feedback controllers to maximize power capture in below-rated conditions and regulate rotor speed during above-rated operation. However, measurements of the approaching wind provided by Light Detection and Ranging (lidar) can be used as part of a preview-based, or feedforward, control system in order to improve rotor speed regulation and reduce structural loads. But the effectiveness of preview-based control depends on how accurately lidar can measure the wind that will interact with the turbine. In this thesis, lidar measurement error is determined using a statistical frequency-domain wind field model including wind evolution, or the change in turbulent wind speeds between the time they are measured and when they reach the turbine. Parameters of the National Renewable Energy Laboratory (NREL) 5-MW reference turbine model are used to determine measurement error for a hub-mounted circularly-scanning lidar scenario, based on commercially-available technology, designed to estimate rotor effective uniform and shear wind speed components. By combining the wind field model, lidar model, and turbine parameters, the optimal lidar scan radius and preview distance that yield the minimum mean square measurement error, as well as the resulting minimum achievable error, are found for a variety of wind conditions. With optimized scan scenarios, it is found that relatively low measurement error can be achieved, but the attainable measurement error largely depends on the wind conditions. In addition, the impact of the induction zone, the region upstream of the turbine where the approaching wind speeds are reduced, as well as turbine yaw error on measurement quality is analyzed. In order to minimize the mean square measurement error, an optimal measurement prefilter is employed, which depends on statistics of the correlation between the preview measurements and the wind that interacts with the turbine. However, because the wind speeds encountered by the turbine are unknown, a Kalman filter-based wind speed estimator is developed that relies on turbine sensor outputs. Using simulated lidar measurements in conjunction with wind speed estimator outputs based on aeroelastic simulations of the NREL 5-MW turbine model, it is shown how the optimal prefilter can adapt to varying degrees of measurement quality.

@PhdThesis{Simley2015,
Title = {Wind Speed Preview Measurement and Estimation for Feedforward Control of Wind Turbines},
Author = {Simley, Eric},
School = {University of Colorado at Boulder},
Year = {2015},
Abstract = {Wind turbines typically rely on feedback controllers to maximize power capture in below-rated conditions and regulate rotor speed during above-rated operation. However, measurements of the approaching wind provided by Light Detection and Ranging (lidar) can be used as part of a preview-based, or feedforward, control system in order to improve rotor speed regulation and reduce structural loads. But the effectiveness of preview-based control depends on how accurately lidar can measure the wind that will interact with the turbine.
In this thesis, lidar measurement error is determined using a statistical frequency-domain wind field model including wind evolution, or the change in turbulent wind speeds between the time they are measured and when they reach the turbine. Parameters of the National Renewable Energy Laboratory (NREL) 5-MW reference turbine model are used to determine measurement error for a hub-mounted circularly-scanning lidar scenario, based on commercially-available technology, designed to estimate rotor effective uniform and shear wind speed components. By combining the wind field model, lidar model, and turbine parameters, the optimal lidar scan radius and preview distance that yield the minimum mean square measurement error, as well as the resulting minimum achievable error, are found for a variety of wind conditions. With optimized scan scenarios, it is found that relatively low measurement error can be achieved, but the attainable measurement error largely depends on the wind conditions. In addition, the impact of the induction zone, the region upstream of the turbine where the approaching wind speeds are reduced, as well as turbine yaw error on measurement quality is analyzed.
In order to minimize the mean square measurement error, an optimal measurement prefilter is employed, which depends on statistics of the correlation between the preview measurements and the wind that interacts with the turbine. However, because the wind speeds encountered by the turbine are unknown, a Kalman filter-based wind speed estimator is developed that relies on turbine sensor outputs. Using simulated lidar measurements in conjunction with wind speed estimator outputs based on aeroelastic simulations of the NREL 5-MW turbine model, it is shown how the optimal prefilter can adapt to varying degrees of measurement quality.},
Url = {http://pqdtopen.proquest.com/doc/1719284807.html?FMT=ABS}
}

• L. Vollmer, M. van Dooren, D. Trabucchi, J. Schneemann, G. Steinfeld, B. Witha, J. Trujillo, and M. Kühn, “First comparison of les of an offshore wind turbine wake with dual-doppler lidar measurements in a german offshore wind farm,” Journal of physics: conference series, vol. 625, iss. 1, pp. 12001-12001, 2015. doi:10.1088/1742-6596/625/1/012001
Large-Eddy Simulations (LES) are more and more used for simulating wind turbine wakes as they resolve the atmospheric as well as the wake turbulence. Considering the expenses and sparsity of offshore measurements, LES can provide valuable insights into the flow field in offshore wind farms. However, for an application of LES wind fields to assess offshore wind farm flow, a proper validation with measured data is necessary. Such a proper validation requires that the LES can closely reproduce the atmospheric conditions during the measurement. For this purpose, a representation of the large-scale features that drive the wind flow is required. Large-scale-forcing and nudging of the LES model PALM is tested with reanalysis data of the COSMO-DE model for a case study during one particular day in the beginning of 2014 at a German offshore wind farm. As wind and temperature profiles of the LES prove to follow the large-scale features closely, the wake of a single wind turbine is simulated with an advanced version of an actuator disc model. Measurement data is provided by processed dual-Doppler lidar measurements during the same day in the same wind farm. Several methods have been investigated at the University of Oldenburg to compare LES wind fields and lidar measurements. In this study a dual-Doppler algorithm was applied in order to estimate the horizontal stationary wind field. The raw data originate from Plan Position Indicator (PPI) measurements, which have been performed with two long-range wind lidars installed at different opposing platforms at the border of the wind farm.

@Article{Vollmer2015,
Title = {First comparison of LES of an offshore wind turbine wake with dual-Doppler lidar measurements in a German offshore wind farm},
Author = {Vollmer, L and van Dooren, M and Trabucchi, D and Schneemann, J and Steinfeld, G and Witha, B and Trujillo, J and Kühn, M},
Journal = {Journal of Physics: Conference Series},
Year = {2015},
Number = {1},
Pages = {012001--012001},
Volume = {625},
Abstract = {Large-Eddy Simulations (LES) are more and more used for simulating wind turbine wakes as they resolve the atmospheric as well as the wake turbulence. Considering the expenses and sparsity of offshore measurements, LES can provide valuable insights into the flow field in offshore wind farms. However, for an application of LES wind fields to assess offshore wind farm flow, a proper validation with measured data is necessary. Such a proper validation requires that the LES can closely reproduce the atmospheric conditions during the measurement. For this purpose, a representation of the large-scale features that drive the wind flow is required. Large-scale-forcing and nudging of the LES model PALM is tested with reanalysis data of the COSMO-DE model for a case study during one particular day in the beginning of 2014 at a German offshore wind farm. As wind and temperature profiles of the LES prove to follow the large-scale features closely, the wake of a single wind turbine is simulated with an advanced version of an actuator disc model. Measurement data is provided by processed dual-Doppler lidar measurements during the same day in the same wind farm. Several methods have been investigated at the University of Oldenburg to compare LES wind fields and lidar measurements. In this study a dual-Doppler algorithm was applied in order to estimate the horizontal stationary wind field. The raw data originate from Plan Position Indicator (PPI) measurements, which have been performed with two long-range wind lidars installed at different opposing platforms at the border of the wind farm.},
Doi = {10.1088/1742-6596/625/1/012001},
Url = {http://dx.doi.org/10.1088/1742-6596/625/1/012001}
}

• H. Wang, R. J. Barthelmie, A. Clifton, and S. C. Pryor, “Wind measurements from arc scans with doppler wind lidar,” Journal of atmospheric and oceanic technology, vol. 32, iss. 11, pp. 2024-2040, 2015. doi:10.1175/JTECH-D-14-00059.1
Defining optimal scanning geometries for scanning lidars for wind energy applications remains an active field of research. This paper evaluates uncertainties associated with arc scan geometries and presents recommendations regarding optimal configurations in the atmospheric boundary layer. The analysis is based on arc scan data from a Doppler wind lidar with one elevation angle and seven azimuth angles spanning 30° and focuses on an estimation of 10-min mean wind speed and direction. When flow is horizontally uniform, this approach can provide accurate wind measurements required for wind resource assessments in part because of its high resampling rate. Retrieved wind velocities at a single range gate exhibit good correlation to data from a sonic anemometer on a nearby meteorological tower, and vertical profiles of horizontal wind speed, though derived from range gates located on a conical surface, match those measured by mast-mounted cup anemometers. Uncertainties in the retrieved wind velocity are related to high turbulent wind fluctuation and an inhomogeneous horizontal wind field. The radial velocity variance is found to be a robust measure of the uncertainty of the retrieved wind speed because of its relationship to turbulence properties. It is further shown that the standard error of wind speed estimates can be minimized by increasing the azimuthal range beyond 30° and using five to seven azimuth angles.

@Article{Wang2015,
Title = {Wind Measurements from Arc Scans with Doppler Wind Lidar},
Author = {H. Wang and R. J. Barthelmie and A. Clifton and S. C. Pryor},
Journal = {Journal of Atmospheric and Oceanic Technology},
Year = {2015},
Number = {11},
Pages = {2024-2040},
Volume = {32},
Abstract = {Defining optimal scanning geometries for scanning lidars for wind energy applications remains an active field of research. This paper evaluates uncertainties associated with arc scan geometries and presents recommendations regarding optimal configurations in the atmospheric boundary layer. The analysis is based on arc scan data from a Doppler wind lidar with one elevation angle and seven azimuth angles spanning 30° and focuses on an estimation of 10-min mean wind speed and direction. When flow is horizontally uniform, this approach can provide accurate wind measurements required for wind resource assessments in part because of its high resampling rate. Retrieved wind velocities at a single range gate exhibit good correlation to data from a sonic anemometer on a nearby meteorological tower, and vertical profiles of horizontal wind speed, though derived from range gates located on a conical surface, match those measured by mast-mounted cup anemometers. Uncertainties in the retrieved wind velocity are related to high turbulent wind fluctuation and an inhomogeneous horizontal wind field. The radial velocity variance is found to be a robust measure of the uncertainty of the retrieved wind speed because of its relationship to turbulence properties. It is further shown that the standard error of wind speed estimates can be minimized by increasing the azimuthal range beyond 30° and using five to seven azimuth angles.},
Doi = {10.1175/JTECH-D-14-00059.1},
Eprint = {

http://dx.doi.org/10.1175/JTECH-D-14-00059.1

},
Url = {http://dx.doi.org/10.1175/JTECH-D-14-00059.1}
}

### 2014

• M. L. Aitken, R. M. Banta, Y. L. Pichugina, and J. K. Lundquist, “Quantifying wind turbine wake characteristics from scanning remote sensor data,” Journal of atmospheric and oceanic technology, vol. 31, iss. 4, pp. 765-787, 2014. doi:10.1175/JTECH-D-13-00104.1
Because of the dense arrays at most wind farms, the region of disturbed flow downstream of an individual turbine leads to reduced power production and increased structural loading for its leeward counterparts. Currently, wind farm wake modeling, and hence turbine layout optimization, suffers from an unacceptable degree of uncertainty, largely because of a lack of adequate experimental data for model validation. Accordingly, nearly 100 h of wake measurements were collected with long-range Doppler lidar at the National Wind Technology Center at the National Renewable Energy Laboratory in the Turbine Wake and Inflow Characterization Study (TWICS). This study presents quantitative procedures for determining critical parameters from this extensive dataset—such as the velocity deficit, the size of the wake boundary, and the location of the wake centerline—and categorizes the results by ambient wind speed, turbulence, and atmospheric stability. Despite specific reference to lidar, the methodology is general and could be applied to extract wake characteristics from other remote sensor datasets, as well as computational simulation output.The observations indicate an initial velocity deficit of 50\%−60\% immediately behind the turbine, which gradually declines to 15\%−25\% at a downwind distance x of 6.5 rotor diameters (D). The wake expands with downstream distance, albeit less so in the vertical direction due to the presence of the ground: initially the same size as the rotor, the extent of the wake grows to 2.7D (1.2D) in the horizontal (vertical) at x = 6.5D. Moreover, the vertical location of the wake center shifts upward with downstream distance because of the tilt of the rotor.

@Article{Aitken2014,
Title = {Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data},
Author = {Matthew L. Aitken and Robert M. Banta and Yelena L. Pichugina and Julie K. Lundquist},
Journal = {Journal of Atmospheric and Oceanic Technology},
Year = {2014},
Number = {4},
Pages = {765-787},
Volume = {31},
Abstract = {Because of the dense arrays at most wind farms, the region of disturbed flow downstream of an individual turbine leads to reduced power production and increased structural loading for its leeward counterparts. Currently, wind farm wake modeling, and hence turbine layout optimization, suffers from an unacceptable degree of uncertainty, largely because of a lack of adequate experimental data for model validation. Accordingly, nearly 100 h of wake measurements were collected with long-range Doppler lidar at the National Wind Technology Center at the National Renewable Energy Laboratory in the Turbine Wake and Inflow Characterization Study (TWICS). This study presents quantitative procedures for determining critical parameters from this extensive dataset—such as the velocity deficit, the size of the wake boundary, and the location of the wake centerline—and categorizes the results by ambient wind speed, turbulence, and atmospheric stability. Despite specific reference to lidar, the methodology is general and could be applied to extract wake characteristics from other remote sensor datasets, as well as computational simulation output.The observations indicate an initial velocity deficit of 50\%−60\% immediately behind the turbine, which gradually declines to 15\%−25\% at a downwind distance x of 6.5 rotor diameters (D). The wake expands with downstream distance, albeit less so in the vertical direction due to the presence of the ground: initially the same size as the rotor, the extent of the wake grows to 2.7D (1.2D) in the horizontal (vertical) at x = 6.5D. Moreover, the vertical location of the wake center shifts upward with downstream distance because of the tilt of the rotor.},
Doi = {10.1175/JTECH-D-13-00104.1},
Url = {http://dx.doi.org/10.1175/JTECH-D-13-00104.1}
}

• E. Bossanyi, A. Kumar, and O. Hugues-Salas, “Wind turbine control applications of turbine-mounted lidar,” Journal of physics: conference series, vol. 555, iss. 1, p. 12011, 2014. doi:10.1088/1742-6596/555/1/012011
In recent years there has been much interest in the possible use of LIDAR systems for improving the performance of wind turbine controllers, by providing preview information about the approaching wind field. Various potential benefits have been suggested, and experimental measurements have sometimes been used to claim surprising gains in performance. This paper reports on an independent study which has used detailed analytical methods for two main purposes: firstly to try to evaluate the likely benefits of LIDAR-assisted control objectively, and secondly to provide advice to LIDAR manufacturers about the characteristics of LIDAR systems which are most likely to be of value for this application. Many different LIDAR configurations were compared: as a general conclusion, systems should be able to sample at least 10 points every second, reasonably distributed around the swept area, and allowing a look-ahead time of a few seconds. An important conclusion is that the main benefit of the LIDAR will be to enhance of collective pitch control to reduce thrust-related fatigue loads; there is some indication that extreme loads can also be reduced, but this depends on other considerations which are discussed in the paper. LIDAR-assisted individual pitch control, optimal Cp tracking and yaw control were also investigated, but the benefits over conventional methods are less clear.

@Article{Bossanyi2014,
Title = {Wind turbine control applications of turbine-mounted LIDAR},
Author = {Ervin Bossanyi and Avishek Kumar and Oscar Hugues-Salas},
Journal = {Journal of Physics: Conference Series},
Year = {2014},
Number = {1},
Pages = {012011},
Volume = {555},
Abstract = {In recent years there has been much interest in the possible use of LIDAR systems for improving the performance of wind turbine controllers, by providing preview information about the approaching wind field. Various potential benefits have been suggested, and experimental measurements have sometimes been used to claim surprising gains in performance. This paper reports on an independent study which has used detailed analytical methods for two main purposes: firstly to try to evaluate the likely benefits of LIDAR-assisted control objectively, and secondly to provide advice to LIDAR manufacturers about the characteristics of LIDAR systems which are most likely to be of value for this application. Many different LIDAR configurations were compared: as a general conclusion, systems should be able to sample at least 10 points every second, reasonably distributed around the swept area, and allowing a look-ahead time of a few seconds. An important conclusion is that the main benefit of the LIDAR will be to enhance of collective pitch control to reduce thrust-related fatigue loads; there is some indication that extreme loads can also be reduced, but this depends on other considerations which are discussed in the paper. LIDAR-assisted individual pitch control, optimal Cp tracking and yaw control were also investigated, but the benefits over conventional methods are less clear.},
Doi = {10.1088/1742-6596/555/1/012011},
Url = {http://dx.doi.org/10.1088/1742-6596/555/1/012011}
}

• C. L. Bottasso, P. Pizzinelli, C. E. D. Riboldi, and L. Tasca, “Lidar-enabled model predictive control of wind turbines with real-time capabilities,” Renewable energy, vol. 71, pp. 442-452, 2014. doi:10.1016/j.renene.2014.05.041
We consider LiDAR-enabled model-based collective pitch and torque controllers that can be implemented onboard a wind turbine in a hard real-time environment, in the sense that they can be computed efficiently on standard computer hardware and that require a fixed deterministic number of operations at each call. At first, we show that linear parameter varying wind-scheduled models provide for a reasonable approximation (for control purposes) of the wind turbine response over its entire operating regime. Based on these results, we formulate two model predictive controllers making use of such wind-scheduled linear models and a quadratic cost. The first controller is based on a classical constrained receding horizon approach that leads to the efficient on-line solution of a quadratic problem. The second can be interpreted as its steady-state unconstrained approximation; its implementation is straightforward and leads to the off-line computation of gain matrices that are then wind-scheduled at run time. Both controllers are tested in a high fidelity environment comprising of both a LiDAR and an aeroservoelastic simulator, in deterministic and unfrozen turbulent wind conditions. The numerical experiments show that the receding horizon controller outperforms a standard non-LiDAR-enabled one, as expected and as already reported by other authors. More interestingly, the second simpler controller is shown to provide for an almost similar performance of the more sophisticated one, although at a much lower and trivial computational cost. This behavior is interpreted as being due to the fact that, given the high disturbance level and the frequent solution update, even a rough approximation of the control problem is still capable of capturing the essence of the LiDAR preview information.

@Article{Bottasso2014,
Title = {LiDAR-enabled model predictive control of wind turbines with real-time capabilities},
Author = {C. L. Bottasso and P. Pizzinelli and C. E. D. Riboldi and L. Tasca},
Journal = {Renewable Energy },
Year = {2014},
Pages = {442 - 452},
Volume = {71},
Abstract = {We consider LiDAR-enabled model-based collective pitch and torque controllers that can be implemented onboard a wind turbine in a hard real-time environment, in the sense that they can be computed efficiently on standard computer hardware and that require a fixed deterministic number of operations at each call. At first, we show that linear parameter varying wind-scheduled models provide for a reasonable approximation (for control purposes) of the wind turbine response over its entire operating regime. Based on these results, we formulate two model predictive controllers making use of such wind-scheduled linear models and a quadratic cost. The first controller is based on a classical constrained receding horizon approach that leads to the efficient on-line solution of a quadratic problem. The second can be interpreted as its steady-state unconstrained approximation; its implementation is straightforward and leads to the off-line computation of gain matrices that are then wind-scheduled at run time. Both controllers are tested in a high fidelity environment comprising of both a LiDAR and an aeroservoelastic simulator, in deterministic and unfrozen turbulent wind conditions. The numerical experiments show that the receding horizon controller outperforms a standard non-LiDAR-enabled one, as expected and as already reported by other authors. More interestingly, the second simpler controller is shown to provide for an almost similar performance of the more sophisticated one, although at a much lower and trivial computational cost. This behavior is interpreted as being due to the fact that, given the high disturbance level and the frequent solution update, even a rough approximation of the control problem is still capable of capturing the essence of the LiDAR preview information.},
Doi = {10.1016/j.renene.2014.05.041},
Url = {http://dx.doi.org/10.1016/j.renene.2014.05.041}
}

• A. Clifton and R. Wagner, “Accounting for the effect of turbulence on wind turbine power curves,” Journal of physics: conference series, vol. 524, iss. 1, p. 12109, 2014. doi:10.1088/1742-6596/524/1/012109
Wind turbines require methods to predict the power produced as inflow conditions change. We compare the standard method of binning with a turbulence renormalization method and a machine learning approach using a data set derived from simulations. The method of binning is unable to cope with changes in turbulence; the turbulence renormalization method cannot account for changes in shear other than by using the the equivalent wind speed, which is derived from wind speed data at multiple heights in the rotor disk. The machine learning method is best able to predict the power as conditions change, and could be modified to include additional inflow variables such as veer or yaw error.

@Article{Clifton2014,
Title = {Accounting for the effect of turbulence on wind turbine power curves},
Author = {Andrew Clifton and Rozenn Wagner},
Journal = {Journal of Physics: Conference Series},
Year = {2014},
Number = {1},
Pages = {012109},
Volume = {524},
Abstract = {Wind turbines require methods to predict the power produced as inflow conditions change. We compare the standard method of binning with a turbulence renormalization method and a machine learning approach using a data set derived from simulations. The method of binning is unable to cope with changes in turbulence; the turbulence renormalization method cannot account for changes in shear other than by using the the equivalent wind speed, which is derived from wind speed data at multiple heights in the rotor disk. The machine learning method is best able to predict the power as conditions change, and could be modified to include additional inflow variables such as veer or yaw error.},
Doi = {10.1088/1742-6596/524/1/012109},
Url = {http://dx.doi.org/10.1088/1742-6596/524/1/012109}
}

• F. Dunne, L. Y. Pao, D. Schlipf, and A. K. Scholbrock, “Importance of lidar measurement timing accuracy for wind turbine control,” in Proceedings of the american control conference, Portland, USA, 2014. doi:10.1109/ACC.2014.6859337
A turbine-mounted lidar can measure wind speed ahead of a wind turbine, and this preview measurement can be used to improve turbine control performance by reducing structural loads and/or increasing power capture. Effective lidar-based control requires not only an accurate wind speed measurement, but also knowledge of the expected arrival time of the measured wind. Arrival time is the time it takes for the wind to travel from the measurement focus location to the turbine rotor. Typically, arrival time is assumed to be equal to the distance traveled divided by the average wind speed. Field test data show that this assumption can be improved on average through an induction zone correction. In addition, arrival time can temporarily deviate significantly above or below this average value. If we can anticipate how arrival time will change, we can improve control performance. In this study, we post-process turbine and lidar data to show how arrival time varies and to determine an upper limit on possible improvement as a result of accurately predicting arrival time. Results show that this upper limit is a 26% average increase in coherence bandwidth between the measured wind and the wind that arrives at the rotor. In above-rated wind speeds, for example, this corresponds to a 21% improvement in the performance cost reduction due to incorporating lidar into a blade pitch controller, where the performance cost is a combined measure of generator speed error and blade pitch actuation.

@InProceedings{Dunne2014,
Title = {Importance of lidar measurement timing accuracy for wind turbine control},
Author = {Dunne, Fiona and Pao, Lucy Y. and Schlipf, David and Scholbrock, Andrew K.},
Booktitle = {Proceedings of the American Control Conference},
Year = {2014},
Abstract = {A turbine-mounted lidar can measure wind speed ahead of a wind turbine, and this preview measurement can be used to improve turbine control performance by reducing structural loads and/or increasing power capture. Effective lidar-based control requires not only an accurate wind speed measurement, but also knowledge of the expected arrival time of the measured wind. Arrival time is the time it takes for the wind to travel from the measurement focus location to the turbine rotor. Typically, arrival time is assumed to be equal to the distance traveled divided by the average wind speed. Field test data show that this assumption can be improved on average through an induction zone correction. In addition, arrival time can temporarily deviate significantly above or below this average value. If we can anticipate how arrival time will change, we can improve control performance. In this study, we post-process turbine and lidar data to show how arrival time varies and to determine an upper limit on possible improvement as a result of accurately predicting arrival time. Results show that this upper limit is a 26% average increase in coherence bandwidth between the measured wind and the wind that arrives at the rotor. In above-rated wind speeds, for example, this corresponds to a 21% improvement in the performance cost reduction due to incorporating lidar into a blade pitch controller, where the performance cost is a combined measure of generator speed error and blade pitch actuation.},
Doi = {10.1109/ACC.2014.6859337},
Url = {http://dx.doi.org/10.1109/ACC.2014.6859337}
}

• P. A. Fleming, A. K. Scholbrock, A. Jehu, S. Davoust, E. Osler, A. D. Wright, and A. Clifton, “Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignment,” Journal of physics: conference series, vol. 524, iss. 1, p. 12002, 2014. doi:10.1088/1742-6596/524/1/012002
In this paper, a nacelle-mounted lidar was used to improve the yaw alignment of an experimental wind turbine. Using lidar-recorded data during normal operation, an error correction value for the nacelle vane wind direction measurement used in the yaw controller was determined. A field test was then conducted in which the turbine was operated with and without the correction applied to the yaw controller. Results demonstrated a significant increase in power capture. In addition, the study includes analysis on the impacts on loading of applying this yaw correction. The study demonstrates a successful application in field testing of using a nacelle-mounted lidar to improve turbine performance.

@Article{Fleming2014,
Title = {Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignment},
Author = {P A Fleming and A K Scholbrock and A Jehu and S Davoust and E Osler and A D Wright and A Clifton},
Journal = {Journal of Physics: Conference Series},
Year = {2014},
Number = {1},
Pages = {012002},
Volume = {524},
Abstract = {In this paper, a nacelle-mounted lidar was used to improve the yaw alignment of an experimental wind turbine. Using lidar-recorded data during normal operation, an error correction value for the nacelle vane wind direction measurement used in the yaw controller was determined. A field test was then conducted in which the turbine was operated with and without the correction applied to the yaw controller. Results demonstrated a significant increase in power capture. In addition, the study includes analysis on the impacts on loading of applying this yaw correction. The study demonstrates a successful application in field testing of using a nacelle-mounted lidar to improve turbine performance.},
Doi = {10.1088/1742-6596/524/1/012002},
Url = {http://dx.doi.org/10.1088/1742-6596/524/1/012002}
}

• G. V. Iungo and F. Porté-Agel, “Volumetric scans of wind turbine wakes performed with three simultaneous wind lidars under different atmospheric stability regimes,” Journal of physics: conference series, vol. 524, iss. 1, p. 12164, 2014. doi:10.1088/1742-6596/524/1/012164
Aerodynamic optimization of wind farm layout is a crucial task to reduce wake effects on downstream wind turbines, thus to maximize wind power harvesting. However, downstream evolution and recovery of wind turbine wakes are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, such as wind shear and turbulence intensity, which are in turn affected by the ABL thermal stability. In order to characterize the downstream evolution of wakes produced by full-scale wind turbines under different atmospheric conditions, wind velocity measurements were performed with three wind LiDARs. The volumetric scans are performed by continuously sweeping azimuthal and elevation angles of the LiDARs in order to cover a 3D volume that includes the wind turbine wake. The minimum wake velocity deficit is then evaluated as a function of the downstream location for different atmospheric conditions. It is observed that the ABL thermal stability has a significant effect on the wake evolution, and the wake recovers faster under convective conditions.

@Article{Iungo2014,
Title = {Volumetric scans of wind turbine wakes performed with three simultaneous wind LiDARs under different atmospheric stability regimes},
Author = {Giacomo Valerio Iungo and Fernando Porté-Agel},
Journal = {Journal of Physics: Conference Series},
Year = {2014},
Number = {1},
Pages = {012164},
Volume = {524},
Abstract = {Aerodynamic optimization of wind farm layout is a crucial task to reduce wake effects on downstream wind turbines, thus to maximize wind power harvesting. However, downstream evolution and recovery of wind turbine wakes are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, such as wind shear and turbulence intensity, which are in turn affected by the ABL thermal stability. In order to characterize the downstream evolution of wakes produced by full-scale wind turbines under different atmospheric conditions, wind velocity measurements were performed with three wind LiDARs. The volumetric scans are performed by continuously sweeping azimuthal and elevation angles of the LiDARs in order to cover a 3D volume that includes the wind turbine wake. The minimum wake velocity deficit is then evaluated as a function of the downstream location for different atmospheric conditions. It is observed that the ABL thermal stability has a significant effect on the wake evolution, and the wake recovers faster under convective conditions.},
Doi = {10.1088/1742-6596/524/1/012164},
Url = {http://dx.doi.org/10.1088/1742-6596/524/1/012164}
}

• K. A. Kragh, M. H. Hansen, and L. C. Henriksen, “Sensor comparison study for load alleviating wind turbine pitch control,” Wind energy, vol. 17, iss. 12, pp. 1891-1904, 2014. doi:10.1002/we.1675

@Article{Kragh2014,
Title = {Sensor comparison study for load alleviating wind turbine pitch control},
Author = {Kragh, Knud Abildgaard and Hansen, Morten Hartvig and Henriksen, Lars Christian},
Journal = {Wind Energy},
Year = {2014},
Number = {12},
Pages = {1891--1904},
Volume = {17},
Doi = {10.1002/we.1675},
Url = {http://dx.doi.org/10.1002/we.1675}
}

• S. Raach, D. Schlipf, F. Haizmann, and P. W. Cheng, “Three dimensional dynamic model based wind field reconstruction from lidar data,” Journal of physics: conference series, vol. 524, iss. 1, p. 12005, 2014. doi:10.1088/1742-6596/524/1/012005
Using the inflowing horizontal and vertical wind shears for individual pitch controller is a promising method if blade bending measurements are not available. Due to the limited information provided by a lidar system the reconstruction of shears in real-time is a challenging task especially for the horizontal shear in the presence of changing wind direction. The internal model principle has shown to be a promising approach to estimate the shears and directions in 10 minutes averages with real measurement data. The static model based wind vector field reconstruction is extended in this work taking into account a dynamic reconstruction model based on Taylor’s Frozen Turbulence Hypothesis. The presented method provides time series over several seconds of the wind speed, shears and direction, which can be directly used in advanced optimal preview control. Therefore, this work is an important step towards the application of preview individual blade pitch control under realistic wind conditions. The method is tested using a turbulent wind field and a detailed lidar simulator. For the simulation, the turbulent wind field structure is flowing towards the lidar system and is continuously misaligned with respect to the horizontal axis of the wind turbine. Taylor’s Frozen Turbulence Hypothesis is taken into account to model the wind evolution. For the reconstruction, the structure is discretized into several stages where each stage is reduced to an effective wind speed, superposed with a linear horizontal and vertical wind shear. Previous lidar measurements are shifted using again Taylor’s Hypothesis. The wind field reconstruction problem is then formulated as a nonlinear optimization problem, which minimizes the residual between the assumed wind model and the lidar measurements to obtain the misalignment angle and the effective wind speed and the wind shears for each stage. This method shows good results in reconstructing the wind characteristics of a three dimensional turbulent wind field in real-time, scanned by a lidar system with an optimized trajectory.

@Article{Raach2014,
Title = {Three Dimensional Dynamic Model Based Wind Field Reconstruction from Lidar Data},
Author = {Steffen Raach and David Schlipf and Florian Haizmann and Po Wen Cheng},
Journal = {Journal of Physics: Conference Series},
Year = {2014},
Number = {1},
Pages = {012005},
Volume = {524},
Abstract = {Using the inflowing horizontal and vertical wind shears for individual pitch controller is a promising method if blade bending measurements are not available. Due to the limited information provided by a lidar system the reconstruction of shears in real-time is a challenging task especially for the horizontal shear in the presence of changing wind direction. The internal model principle has shown to be a promising approach to estimate the shears and directions in 10 minutes averages with real measurement data. The static model based wind vector field reconstruction is extended in this work taking into account a dynamic reconstruction model based on Taylor's Frozen Turbulence Hypothesis. The presented method provides time series over several seconds of the wind speed, shears and direction, which can be directly used in advanced optimal preview control. Therefore, this work is an important step towards the application of preview individual blade pitch control under realistic wind conditions. The method is tested using a turbulent wind field and a detailed lidar simulator. For the simulation, the turbulent wind field structure is flowing towards the lidar system and is continuously misaligned with respect to the horizontal axis of the wind turbine. Taylor's Frozen Turbulence Hypothesis is taken into account to model the wind evolution. For the reconstruction, the structure is discretized into several stages where each stage is reduced to an effective wind speed, superposed with a linear horizontal and vertical wind shear. Previous lidar measurements are shifted using again Taylor's Hypothesis. The wind field reconstruction problem is then formulated as a nonlinear optimization problem, which minimizes the residual between the assumed wind model and the lidar measurements to obtain the misalignment angle and the effective wind speed and the wind shears for each stage. This method shows good results in reconstructing the wind characteristics of a three dimensional turbulent wind field in real-time, scanned by a lidar system with an optimized trajectory.},
Doi = {10.1088/1742-6596/524/1/012005},
Url = {http://dx.doi.org/10.1088/1742-6596/524/1/012005}
}

• A. Rettenmeier, D. Schlipf, I. Würth, and P. W. Cheng, “Power performance measurements of the nrel cart-2 wind turbine using a nacelle-based lidar scanner,” Journal of atmospheric and oceanic technology, vol. 31, iss. 10, pp. 2029-2034, 2014. doi:10.1175/JTECH-D-13-00154.1
Different certification procedures in wind energy, such as power performance testing or load estimation, require measurements of the wind speed, which is set in relation to the electrical power output or the turbine loading. The wind shear affects the behavior of the turbine as hub heights and rotor diameters of modern wind turbines increase. Different measurement methods have been developed to take the wind shear into account. In this paper an approach is presented where the wind speed is measured from the nacelle of a wind turbine using a scanning lidar system. The measurement campaign was performed on the two-bladed Controls Advanced Research Turbine (CART-2) at the National Wind Technology Center in Colorado. The wind speed of the turbine inflow was measured and recalculated in three different ways: using an anemometer installed on a meteorological mast, using the nacelle-based lidar scanner, and using the wind turbine itself. Here, the wind speed was recalculated from turbine data using the wind turbine as a big horizontal anemometer. Despite the small number of useful data, the correlation between this so-called rotor effective wind speed and the wind speed measured by the scanning nacelle-based lidar is high. It could be demonstrated that a nacelle-based scanning lidar system provides accurate measurements of the wind speed converted by a wind turbine. This is a first step, and it provides evidence to support further investigations using a much more extensive dataset and refines the parameters in the measurement process.

@Article{Rettenmeier2014,
Title = {Power Performance Measurements of the NREL CART-2 Wind Turbine Using a Nacelle-Based Lidar Scanner},
Author = {Andreas Rettenmeier and David Schlipf and Ines Würth and Po Wen Cheng},
Journal = {Journal of Atmospheric and Oceanic Technology},
Year = {2014},
Number = {10},
Pages = {2029-2034},
Volume = {31},
Abstract = {Different certification procedures in wind energy, such as power performance testing or load estimation, require measurements of the wind speed, which is set in relation to the electrical power output or the turbine loading. The wind shear affects the behavior of the turbine as hub heights and rotor diameters of modern wind turbines increase. Different measurement methods have been developed to take the wind shear into account. In this paper an approach is presented where the wind speed is measured from the nacelle of a wind turbine using a scanning lidar system.
The measurement campaign was performed on the two-bladed Controls Advanced Research Turbine (CART-2) at the National Wind Technology Center in Colorado. The wind speed of the turbine inflow was measured and recalculated in three different ways: using an anemometer installed on a meteorological mast, using the nacelle-based lidar scanner, and using the wind turbine itself. Here, the wind speed was recalculated from turbine data using the wind turbine as a big horizontal anemometer. Despite the small number of useful data, the correlation between this so-called rotor effective wind speed and the wind speed measured by the scanning nacelle-based lidar is high.
It could be demonstrated that a nacelle-based scanning lidar system provides accurate measurements of the wind speed converted by a wind turbine. This is a first step, and it provides evidence to support further investigations using a much more extensive dataset and refines the parameters in the measurement process.},
Doi = {10.1175/JTECH-D-13-00154.1},
Url = {http://dx.doi.org/10.1175/JTECH-D-13-00154.1}
}

• D. Schlipf, P. Fleming, F. Haizmann, A. Scholbrock, M. Hofsäß, A. Wright, and P. W. Cheng, “Field testing of feedforward collective pitch control on the CART2 using a nacelle-based lidar scanner,” Journal of physics: conference series, vol. 555, iss. 1, p. 12090, 2014. doi:10.1088/1742-6596/555/1/012090
This work presents the results from a field test of LIDAR assisted collective pitch control using a scanning LIDAR device installed on the nacelle of a mid-scale research turbine. A nonlinear feedforward controller is extended by an adaptive filter to remove all uncorrelated frequencies of the wind speed measurement to avoid unnecessary control action. Positive effects on the rotor speed regulation as well as on tower, blade and shaft loads have been observed in the case that the previous measured correlation and timing between the wind preview and the turbine reaction are accomplish. The feedforward controller had negative impact, when the LIDAR measurement was disturbed by obstacles in front of the turbine. This work proves, that LIDAR is valuable tool for wind turbine control not only in simulations but also under real conditions. Furthermore, the paper shows that further understanding of the relationship between the wind measurement and the turbine reaction is crucial to improve LIDAR assisted control of wind turbines.

@Article{Schlipf2014,
Title = {Field Testing of Feedforward Collective Pitch Control on the {CART2} Using a Nacelle-Based Lidar Scanner},
Author = {Schlipf, David and Fleming, Paul and Haizmann, Florian and Scholbrock, Andrew and Hofsäß, Martin and Wright, Alan and Cheng, Po Wen},
Journal = {Journal of Physics: Conference Series},
Year = {2014},
Number = {1},
Pages = {012090},
Volume = {555},
Abstract = {This work presents the results from a field test of LIDAR assisted collective pitch control using a scanning LIDAR device installed on the nacelle of a mid-scale research turbine. A nonlinear feedforward controller is extended by an adaptive filter to remove all uncorrelated frequencies of the wind speed measurement to avoid unnecessary control action. Positive effects on the rotor speed regulation as well as on tower, blade and shaft loads have been observed in the case that the previous measured correlation and timing between the wind preview and the turbine reaction are accomplish. The feedforward controller had negative impact, when the LIDAR measurement was disturbed by obstacles in front of the turbine. This work proves, that LIDAR is valuable tool for wind turbine control not only in simulations but also under real conditions. Furthermore, the paper shows that further understanding of the relationship between the wind measurement and the turbine reaction is crucial to improve LIDAR assisted control of wind turbines.},
Doi = {10.1088/1742-6596/555/1/012090},
Url = {http://dx.doi.org/10.1088/1742-6596/555/1/012090}
}

• D. Schlipf and P. W. Cheng, “Flatness-based feedforward control of wind turbines using lidar,” in Proceedings of the 19th world congress of the international federation of automatic control, Cape Town, South Africa, 2014. doi:10.3182/20140824-6-ZA-1003.00443
Current lidar technology is offering a promising opportunity to take a fresh look at wind turbine control. This work evaluates a flatness-based feedforward approach, that allows to calculate the control action based on trajectories of the rotor speed and tower motion using wind measurements. The trajectories are planned online considering actuator constrains to regulate the rotor speed and minimize tower movements. The feedforward signals of the collective pitch and generator torque update can be combined with conventional feedback controllers. This facilitates the application on commercial wind turbines. Simulations using a realistic lidar simulator and a full aero-elastic model show considerable reduction of tower and shaft loads.

@InProceedings{Schlipf2014a,
Title = {Flatness-based Feedforward Control of Wind Turbines Using Lidar},
Author = {David Schlipf and Po Wen Cheng},
Booktitle = {Proceedings of the 19th World Congress of the International Federation of Automatic Control},
Year = {2014},
Address = {Cape Town, South Africa},
Abstract = {Current lidar technology is offering a promising opportunity to take a fresh look at wind turbine control. This work evaluates a flatness-based feedforward approach, that allows to calculate the control action based on trajectories of the rotor speed and tower motion using wind measurements. The trajectories are planned online considering actuator constrains to regulate the rotor speed and minimize tower movements. The feedforward signals of the collective pitch and generator torque update can be combined with conventional feedback controllers. This facilitates the application on commercial wind turbines. Simulations using a realistic lidar simulator and a full aero-elastic model show considerable reduction of tower and shaft loads.},
Doi = {10.3182/20140824-6-ZA-1003.00443},
Url = {http://dx.doi.org/10.3182/20140824-6-ZA-1003.00443}
}

• E. Simley, L. Y. Pao, P. Gebraad, and M. Churchfield, “Investigation of the impact of the upstream induction zone on lidar measurement accuracy for wind turbine control applications using large-eddy simulation,” Journal of physics: conference series, vol. 524, iss. 1, p. 12003, 2014. doi:10.1088/1742-6596/524/1/012003
Several sources of error exist in lidar measurements for feedforward control of wind turbines including the ability to detect only radial velocities, spatial averaging, and wind evolution. This paper investigates another potential source of error: the upstream induction zone. The induction zone can directly affect lidar measurements and presents an opportunity for further decorrelation between upstream wind and the wind that interacts with the rotor. The impact of the induction zone is investigated using the combined CFD and aeroelastic code SOWFA. Lidar measurements are simulated upstream of a 5 MW turbine rotor and the true wind disturbances are found using a wind speed estimator and turbine outputs. Lidar performance in the absence of an induction zone is determined by simulating lidar measurements and the turbine response using the aeroelastic code FAST with wind inputs taken far upstream of the original turbine location in the SOWFA wind field. Results indicate that while measurement quality strongly depends on the amount of wind evolution, the induction zone has little effect. However, the optimal lidar preview distance and circular scan radius change slightly due to the presence of the induction zone.

@Article{Simley2014a,
Title = {Investigation of the Impact of the Upstream Induction Zone on LIDAR Measurement Accuracy for Wind Turbine Control Applications using Large-Eddy Simulation},
Author = {Eric Simley and Lucy Y Pao and Pieter Gebraad and Matthew Churchfield},
Journal = {Journal of Physics: Conference Series},
Year = {2014},
Number = {1},
Pages = {012003},
Volume = {524},
Abstract = {Several sources of error exist in lidar measurements for feedforward control of wind turbines including the ability to detect only radial velocities, spatial averaging, and wind evolution. This paper investigates another potential source of error: the upstream induction zone. The induction zone can directly affect lidar measurements and presents an opportunity for further decorrelation between upstream wind and the wind that interacts with the rotor. The impact of the induction zone is investigated using the combined CFD and aeroelastic code SOWFA. Lidar measurements are simulated upstream of a 5 MW turbine rotor and the true wind disturbances are found using a wind speed estimator and turbine outputs. Lidar performance in the absence of an induction zone is determined by simulating lidar measurements and the turbine response using the aeroelastic code FAST with wind inputs taken far upstream of the original turbine location in the SOWFA wind field. Results indicate that while measurement quality strongly depends on the amount of wind evolution, the induction zone has little effect. However, the optimal lidar preview distance and circular scan radius change slightly due to the presence of the induction zone.},
Doi = {10.1088/1742-6596/524/1/012003},
Url = {http://dx.doi.org/10.1088/1742-6596/524/1/012003}
}

• E. Simley, L. Y. Pao, R. Frehlich, B. Jonkman, and N. Kelley, “Analysis of light detection and ranging wind speed measurements for wind turbine control,” Wind energy, vol. 17, iss. 3, pp. 413-433, 2014. doi:10.1002/we.1584
Light detection and ranging (LIDAR) systems are able to measure the speed of incoming wind before it reaches a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine structural loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. This study examines the accuracy of different measurement scenarios that rely on coherent continuous-wave or pulsed Doppler LIDAR systems, in terms of root-mean-square measurement error, to determine their applicability to feedforward control. In particular, the impacts of measurement range, angular offset of the LIDAR beam from the wind direction, and measurement noise are studied for various wind conditions. A realistic simulation case involving a scanning LIDAR unit mounted in the spinner of a MW-scale wind turbine is studied in depth, with emphasis on preview distances that provide minimum measurement error for a specific scan radius. Measurement error is analyzed for LIDAR-based estimates of point wind speeds at the rotor as well as spanwise averaged blade effective wind speeds. The impact of turbulence structures with high coherent turbulent kinetic energy on measurement error is discussed as well. Copyright © 2013 John Wiley & Sons, Ltd.

@Article{Simley2014b,
Title = {Analysis of light detection and ranging wind speed measurements for wind turbine control},
Author = {Simley, Eric and Pao, Lucy Y. and Frehlich, Rod and Jonkman, Bonnie and Kelley, Neil},
Journal = {Wind Energy},
Year = {2014},
Number = {3},
Pages = {413--433},
Volume = {17},
Abstract = {Light detection and ranging (LIDAR) systems are able to measure the speed of incoming wind before it reaches a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine structural loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. This study examines the accuracy of different measurement scenarios that rely on coherent continuous-wave or pulsed Doppler LIDAR systems, in terms of root-mean-square measurement error, to determine their applicability to feedforward control. In particular, the impacts of measurement range, angular offset of the LIDAR beam from the wind direction, and measurement noise are studied for various wind conditions. A realistic simulation case involving a scanning LIDAR unit mounted in the spinner of a MW-scale wind turbine is studied in depth, with emphasis on preview distances that provide minimum measurement error for a specific scan radius. Measurement error is analyzed for LIDAR-based estimates of point wind speeds at the rotor as well as spanwise averaged blade effective wind speeds. The impact of turbulence structures with high coherent turbulent kinetic energy on measurement error is discussed as well. Copyright © 2013 John Wiley & Sons, Ltd.},
Doi = {10.1002/we.1584},
Url = {http://dx.doi.org/10.1002/we.1584}
}

• D. Trabucchi, J. Trujillo, J. Schneemann, M. Bitter, and M. Kühn, “Application of staring lidars to study the dynamics of wind turbine wakes,” Meteorologische zeitschrift, vol. 24, iss. 6, pp. 557-564, 2014. doi:10.1127/metz/2014/0610
Standard anemometry or vertical profiling remote sensing are not always a convenient approach to study the dynamics of wind turbines wake. One or more lidar windscanner can be applied for this purpose. In this paper a measurement strategy is presented, which permits the characterization of the wake dynamics using two long range wind lidars operated in a stationary mode. In this approach two pulsed devices are staring with low elevation obliquely across the wake. The lidar beams are supposed to cross each other on the downstream axis of the wake to perform simultaneous measurements in the wake field from side to side. The deflection of the wake is identified fitting a model to the average data. Spectral analysis provide the frequency content of the measurements at different distances from the wake center. This setup was implemented in a full-field measurement campaign where the wake of a multi-MW wind turbine was analysed. The tracking of the wake centre was applied successfully to this measurement. Moreover the spectral analysis showed increased energy content close to the wake lateral edges. This can be connected both to the higher turbulence level due to the tip vorteces and to the large scale dynamics of the wake.

@Article{Trabucchi2014,
Title = {Application of staring lidars to study the dynamics of wind turbine wakes},
Author = {Trabucchi, Davide and Trujillo, Juan-José and Schneemann, Jörge and Bitter, Martin and Kühn, Martin},
Journal = {Meteorologische Zeitschrift},
Year = {2014},
Number = {6},
Pages = {557 - 564},
Volume = {24},
Abstract = {Standard anemometry or vertical profiling remote sensing are not always a convenient approach to study the dynamics of wind turbines wake. One or more lidar windscanner can be applied for this purpose. In this paper a measurement strategy is presented, which permits the characterization of the wake dynamics using two long range wind lidars operated in a stationary mode. In this approach two pulsed devices are staring with low elevation obliquely across the wake. The lidar beams are supposed to cross each other on the downstream axis of the wake to perform simultaneous measurements in the wake field from side to side. The deflection of the wake is identified fitting a model to the average data. Spectral analysis provide the frequency content of the measurements at different distances from the wake center. This setup was implemented in a full-field measurement campaign where the wake of a multi-MW wind turbine was analysed. The tracking of the wake centre was applied successfully to this measurement. Moreover the spectral analysis showed increased energy content close to the wake lateral edges. This can be connected both to the higher turbulence level due to the tip vorteces and to the large scale dynamics of the wake.},
Doi = {10.1127/metz/2014/0610},
Url = {http://dx.doi.org/10.1127/metz/2014/0610}
}

• N. Vasiljevic, “A time-space synchronization of coherent doppler scanning lidars for 3d measurements of wind fields,” Technical University of Denmark, Ph.D. Thesis PhD-0027 (EN), 2014.
This thesis consists of the results of a Ph.D. study that was focused on the development of the system of three time-space synchronized pulsed coherent Doppler scanning lidars, which are coordinated by a remote ’master computer’. This system has the unique capability to measure a complete three-dimensional flow field by emitting the laser beams from the three spatially separated lidars, directing them to intersect, and moving the beam intersection over an area of interest. Each individual lidar was engineered to be powered by two real servo motors, and one virtual stepper motor. The stepper motor initiates the laser pulse emission and acquisition of the backscattered light, while the two servo motors conduct the scanner head rotation that provides means to direct the laser pulses into the atmosphere. By controlling the rotation of the three motors from the motion controller the strict synchronization and time control of the emission, steering and acquisition were achieved, resulting that the complete lidar measurement process is controlled from the single hardware component. The system was formed using a novel approach, in which the master computer simultaneously coordinates the remote lidars through a UDP/IP and TCP/IP network by exchange of network packets. Since the size of the packets is roughly 1 kB, this approach allows an uninterrupted and fast coordination of the lidars, even in the case of mobile networks such as GSM. With this approach a maximum lag of 10 ms was observed in terms of the scanner heads’ rotation and the measurements among the lidars in the system. The laser beam pointing accuracy of each lidar was estimated to ±0.5° for the laser beam direction, and roughly ±5 m for the sensing distance. A set of procedures were proposed that can improve the pointing accuracy by a factor of 20. Subsequently, two experiments were carried out in which the developed multiple lidars system was used to synchronously measure wind velocity fields in multiple points in the atmosphere.

@TechReport{Vasiljevic2014,
Title = {A time-space synchronization of coherent Doppler scanning lidars for 3D measurements of wind fields},
Author = {N. Vasiljevic},
Institution = {Technical University of Denmark},
Year = {2014},
Number = {PhD-0027 (EN)},
Type = {Ph.D. Thesis},
Abstract = {This thesis consists of the results of a Ph.D. study that was focused on the development of the system of three time-space synchronized pulsed coherent Doppler scanning lidars, which are coordinated by a remote ’master computer’. This system has the unique capability to measure a complete three-dimensional flow field by emitting the laser beams from the three spatially separated lidars, directing them to intersect, and moving the beam intersection over an area of interest. Each individual lidar was engineered to be powered by two real servo motors, and one virtual stepper motor. The stepper motor initiates the laser pulse emission and acquisition of the backscattered light, while the two servo motors conduct the scanner head rotation that provides means to direct the laser pulses into the atmosphere. By controlling the rotation of the three motors from the motion controller the strict synchronization and time control of the emission, steering and acquisition were achieved, resulting that the complete lidar measurement process is controlled from the single hardware component. The system was formed using a novel approach, in which the master computer simultaneously coordinates the remote lidars through a UDP/IP and TCP/IP network by exchange of network packets. Since the size of the packets is roughly 1 kB, this approach allows an uninterrupted and fast coordination of the lidars, even in the case of mobile networks such as GSM. With this approach a maximum lag of 10 ms was observed in terms of the scanner heads’ rotation and the measurements among the lidars in the system. The laser beam pointing accuracy of each lidar was estimated to ±0.5° for the laser beam direction, and roughly ±5 m for the sensing distance. A set of procedures were proposed that can improve the pointing accuracy by a factor of 20. Subsequently, two experiments were carried out in which the developed multiple lidars system was used to synchronously measure wind velocity fields in multiple points in the atmosphere.},
Url = {http://orbit.dtu.dk/en/publications/a-timespace-synchronization-of-coherent-doppler-scanning-lidars-for-3d-measurements-of-wind-fields%28e2519d99-5846-4651-947d-38c287452366%29.html}
}

• R. Wagner, B. Cañadillas, A. Clifton, S. Feeney, N. Nygaard, M. Poodt, S. C. Martin, E. Tüxen, and J. Wagenaar, “Rotor equivalent wind speed for power curve measurement: comparative exercise for iea wind annex 32,” Journal of physics: conference series, vol. 524, iss. 1, p. 12108, 2014. doi:10.1088/1742-6596/524/1/012108
A comparative exercise has been organised within the International Energy Agency (IEA) Wind Annex 32 in order to test the Rotor Equivalent Wind Speed (REWS) method under various conditions of wind shear and measurement techniques. Eight organisations from five countries participated in the exercise. Each member of the group has derived both the power curve based on the wind speed at hub height and the power curve based on the REWS. This yielded results for different wind turbines, located in diverse types of terrain and where the wind speed profile was measured with different instruments (mast or various lidars). The participants carried out two preliminary steps in order to reach consensus on how to implement the REWS method. First, they all derived the REWS for one 10 minute wind speed profile. Secondly, they all derived the power curves for one dataset. The main point requiring consensus was the definition of the segment area used as weighting for the wind speeds measured at the various heights in the calculation of the REWS. This comparative exercise showed that the REWS method results in a significant difference compared to the standard method using the wind speed at hub height in conditions with large shear and low turbulence intensity.

@Article{Wagner2014,
Title = {Rotor equivalent wind speed for power curve measurement: comparative exercise for IEA Wind Annex 32},
Author = {R Wagner and B Cañadillas and A Clifton and S Feeney and N Nygaard and M Poodt and C St Martin and E Tüxen and JW Wagenaar},
Journal = {Journal of Physics: Conference Series},
Year = {2014},
Number = {1},
Pages = {012108},
Volume = {524},
Abstract = {A comparative exercise has been organised within the International Energy Agency (IEA) Wind Annex 32 in order to test the Rotor Equivalent Wind Speed (REWS) method under various conditions of wind shear and measurement techniques. Eight organisations from five countries participated in the exercise. Each member of the group has derived both the power curve based on the wind speed at hub height and the power curve based on the REWS. This yielded results for different wind turbines, located in diverse types of terrain and where the wind speed profile was measured with different instruments (mast or various lidars). The participants carried out two preliminary steps in order to reach consensus on how to implement the REWS method. First, they all derived the REWS for one 10 minute wind speed profile. Secondly, they all derived the power curves for one dataset. The main point requiring consensus was the definition of the segment area used as weighting for the wind speeds measured at the various heights in the calculation of the REWS. This comparative exercise showed that the REWS method results in a significant difference compared to the standard method using the wind speed at hub height in conditions with large shear and low turbulence intensity.},
Doi = {10.1088/1742-6596/524/1/012108},
Url = {http://dx.doi.org/10.1088/1742-6596/524/1/012108}
}

• N. Wang, K. E. Johnson, A. D. Wright, and C. E. Carcangiu, “Lidar-assisted wind turbine feedforward torque controller design below rated,” in Proceedings of the american control conference, Portland, OR, USA, 2014. doi:10.1109/ACC.2014.6859039
Two below-rated feedforward torque control strategies are investigated for a megawatt-scale commercial turbine to regulate rotor speed and reduce turbine structural loads. The investigated regime for this research is the transition region when the turbine is operating just below rated wind speed. In this transition region, the rotor speed has reached its rated value but the generator torque has not. The first of the two controllers designed is a nonlinear feedforward controller that is based on aerodynamic torque balance computed from a light detection and ranging (lidar) measurement and a feedback power measurement from the turbine. A linear disturbance accommodating control (DAC) based torque controller has also been adapted and compared with the nonlinear control strategy. This DAC depends only on the lidar feedforward measurement without using feedback measurement from the turbine. The controllers are verified with a nonlinear FAST turbine model and turbulent wind fields.

@InProceedings{Wang2014,
Title = {Lidar-assisted wind turbine feedforward torque controller design below rated},
Author = {Wang, Na and Johnson, Kathryn E. and Wright, Alan D. and Carcangiu, Carlo E.},
Booktitle = {Proceedings of the American Control Conference},
Year = {2014},
Abstract = {Two below-rated feedforward torque control strategies are investigated for a megawatt-scale commercial turbine to regulate rotor speed and reduce turbine structural loads. The investigated regime for this research is the transition region when the turbine is operating just below rated wind speed. In this transition region, the rotor speed has reached its rated value but the generator torque has not. The first of the two controllers designed is a nonlinear feedforward controller that is based on aerodynamic torque balance computed from a light detection and ranging (lidar) measurement and a feedback power measurement from the turbine. A linear disturbance accommodating control (DAC) based torque controller has also been adapted and compared with the nonlinear control strategy. This DAC depends only on the lidar feedforward measurement without using feedback measurement from the turbine. The controllers are verified with a nonlinear FAST turbine model and turbulent wind fields.},
Doi = {10.1109/ACC.2014.6859039},
Url = {http://dx.doi.org/10.1109/ACC.2014.6859039}
}

### 2013

• M. Asimakopoulos, P. Clive, G. More, and R. Boddington, “Offshore compression zone measurement and visualisation,” in Proceedings of the european wind energy association annual event, Barcelona, Spain, 2013.
The determination of the length of the compression zone in front of a wind turbine is critical to obtaining accurate power curve tests. Compression zone length defines the minimum distance where a meteorological mast can be installed for power curve testing. Conventionally it is recommended that meteorological masts are installed between 2 and 4 (and preferably 2.5) rotor diameters away from the wind turbine to be tested. However, research has indicated that this may not be adequate and that meteorological masts may be reading compression zone affected wind speeds. To investigate this, SgurrEnergy has conducted a comprehensive measurement campaign where Galion Lidar units have been installed on an offshore wind turbine, recording data in a mode whereby the Galion Lidar is measuring the incoming wind speed. Two different configurations were used, the first one provides a simplified visualisation of the incoming wind speed profile, whereas the second configuration is a contour plot showing how wind speed varies across the horizontal plane at hub height and along a distance of 1 km in front of the rotor. In both cases, results show that at 2.5 rotor diameters the compression zone is still evident with wind speed reading varying between 1-3% from the free stream wind speed. In addition, the influence of the compression zone may extend until approximately 3.5 rotor diameters depending on the wind speed. Therefore, it is possible that current power curve calculation methods are leading to an overestimation of annual energy yield prediction.

@InProceedings{Asimakopoulos2014,
Title = {Offshore compression zone measurement and visualisation},
Author = {Asimakopoulos, M. and Clive, P. and More, G. and Boddington, R.},
Booktitle = {Proceedings of the European Wind Energy Association annual event},
Year = {2013},
Abstract = {The determination of the length of the compression zone in front of a wind turbine is critical to obtaining accurate power curve tests. Compression zone length defines the minimum distance where a meteorological mast can be installed for power curve testing.
Conventionally it is recommended that meteorological masts are installed between 2 and 4 (and preferably 2.5) rotor diameters away from the wind turbine to be tested. However, research has indicated that this may not be adequate and that meteorological masts may be reading compression zone affected wind speeds.
To investigate this, SgurrEnergy has conducted a comprehensive measurement campaign where Galion Lidar units have been installed on an offshore wind turbine, recording data in a mode whereby the Galion Lidar is measuring the incoming wind speed. Two different configurations were used, the first one provides a simplified visualisation of the incoming wind speed profile, whereas the second configuration is a contour plot showing how wind speed varies across the horizontal plane at hub height and along a distance of 1 km in front of the rotor.
In both cases, results show that at 2.5 rotor diameters the compression zone is still evident with wind speed reading varying between 1-3% from the free stream wind speed. In addition, the influence of the compression zone may extend until approximately 3.5 rotor diameters depending on the wind speed.
Therefore, it is possible that current power curve calculation methods are leading to an overestimation of annual energy yield prediction.},
}

• J. Berg, J. Mann, and E. Patton, “Lidar-Observed stress vectors and veer in the atmospheric boundary layer,” Journal of atmospheric and oceanic technology, vol. 30, iss. 9, pp. 1961-1969, 2013. doi:10.1175/JTECH-D-12-00266.1
This study demonstrates that a pulsed wind lidar is a reliable instrument for measuring angles between horizontal vectors of significance in the atmospheric boundary layer. Three different angles are considered: the wind turning, the angle between the stress vector and the mean wind direction, and the angle between the stress vector and the vertical gradient of the mean velocity vector. The latter is assumed to be zero by the often applied turbulent-viscosity hypothesis, so that the stress vector can be described through the vertical gradient of velocity. In the atmospheric surface layer, where the Coriolis force is negligible, this is supposedly a good approximation. High-resolution large-eddy simulation data show that this is indeed the case even beyond the surface layer. In contrast, through analysis of WindCube lidar measurements supported by sonic measurements, the study shows that it is only valid very close to the surface. The deviation may be significant even at 100 m. This behavior is attributed to mesoscale effects.

@Article{Berg2013,
Title = {{Lidar-Observed stress vectors and veer in the atmospheric boundary layer}},
Author = {J. Berg and J. Mann and E. Patton},
Journal = {Journal of atmospheric and oceanic technology},
Year = {2013},
Number = {9},
Pages = {1961-1969},
Volume = {30},
Abstract = {This study demonstrates that a pulsed wind lidar is a reliable instrument for measuring angles between horizontal vectors of significance in the atmospheric boundary layer. Three different angles are considered: the wind turning, the angle between the stress vector and the mean wind direction, and the angle between the stress vector and the vertical gradient of the mean velocity vector. The latter is assumed to be zero by the often applied turbulent-viscosity hypothesis, so that the stress vector can be described through the vertical gradient of velocity. In the atmospheric surface layer, where the Coriolis force is negligible, this is supposedly a good approximation. High-resolution large-eddy simulation data show that this is indeed the case even beyond the surface layer. In contrast, through analysis of WindCube lidar measurements supported by sonic measurements, the study shows that it is only valid very close to the surface. The deviation may be significant even at 100 m. This behavior is attributed to mesoscale effects.},
Doi = {10.1175/JTECH-D-12-00266.1},
Url = {http://dx.doi.org/10.1175/JTECH-D-12-00266.1}
}

• E. Bossanyi, “Un-freezing the turbulence: application to lidar-assisted wind turbine control,” Iet renewable power generation, vol. 7, iss. 4, pp. 321-329, 2013. doi:10.1049/iet-rpg.2012.0260
Recent developments in LiDAR technology have led to much interest in the possibility of improving turbine control by using a turbine-mounted LiDAR, to provide advance information about the approaching wind field. This could significantly reduce turbine loads, bringing improved cost-effectiveness, especially for large turbines. There have also been claims of direct increases in energy capture as a result of using such preview information. This study reports on an independent study employing detailed analytical methods to evaluate the likely benefits of LiDAR-assisted control and advise LiDAR manufacturers about the characteristics of their systems, which are most likely to be useful for this application. Accurate simulation models are vital for assessing the performance of LiDARs and controllers which use them. Current models use Taylor’s frozen turbulence hypothesis, but this is not strictly valid when LiDAR is used to measure upstream wind speeds, as the measured wind cannot be assumed to convect unchanged to the turbine. A method for avoiding the frozen turbulence assumption is proposed, and simulation results are presented to illustrate the effect on fatigue load reductions which LiDARassisted control might achieve. A detailed assessment of possible LiDAR benefits is made using the UPWIND generic 5 MW turbine as an example.

@Article{Bossanyi2012,
Title = {Un-freezing the turbulence: application to LiDAR-assisted wind turbine control},
Author = {Ervin Bossanyi},
Journal = {IET Renewable Power Generation},
Year = {2013},
Number = {4},
Pages = {321-329},
Volume = {7},
Abstract = {Recent developments in LiDAR technology have led to much interest in the possibility of improving turbine control by using a turbine-mounted LiDAR, to provide advance information about the approaching wind field. This could significantly reduce turbine loads, bringing improved cost-effectiveness, especially for large turbines. There have also been claims of direct increases in energy capture as a result of using such preview information. This study reports on an independent study employing detailed analytical methods to evaluate the likely benefits of LiDAR-assisted control and advise LiDAR manufacturers about the characteristics of their systems, which are most likely to be useful for this application. Accurate simulation models are vital for assessing the performance of LiDARs and controllers which use them. Current models use Taylor's frozen turbulence hypothesis, but this is not strictly valid when LiDAR is used to measure upstream wind speeds, as the measured wind cannot be assumed to convect unchanged to the turbine. A method for avoiding the frozen turbulence assumption is proposed, and simulation results are presented to illustrate the effect on fatigue load reductions which LiDARassisted control might achieve. A detailed assessment of possible LiDAR benefits is made using the UPWIND generic 5 MW turbine as an example.},
Doi = {10.1049/iet-rpg.2012.0260},
Url = {http://dx.doi.org/10.1049/iet-rpg.2012.0260}
}

• A. Clifton, D. Elliott, and M. Courtney., “Iea wind rp 15. ground-based vertically-profyling remote sensing for wind resource assessment,” IEA wind, RP 15, 2013.
Vertically-profiling wind remote sensing technologies such as lidar and sodar potentially allow the collection of wind speed and direction data at heights up to typical wind turbine hubs, and beyond. Traditionally, wind data have been collected using cup anemometers and vanes on meteorological towers. It is therefore necessary to compare and verify the performance of sodar and lidar to these traditional traceable technologies and standards before using the data for a wind resource assessment. Care also needs to be taken that the remote sensing device is correctly installed, operated and maintained. This document includes recommended practices for the characterization, verification, installation, operation and maintenance, and data analysis of a remote sensing device for the purposes of wind energy assessments. Recommended practices for vertically-profiling lidar and sodar are often identical, and so only recommended practices that differ between the two technologies refer to a specific technology. The following is a broad summary of the specific recommended practices in this document: – Remote sensing device technologies, techniques and methods should be clearly characterized by manufacturers. – Remote sensing devices should be verified periodically against calibrated reference devices that can be traced back to a national standard. – Remote sensing devices should be installed by trained personnel following an installation checklist. – The use of the remote sensing device should be documented, including verification activities, site deployments, servicing and data analysis. – Remote sensing devices should be able to operate reliably and accurately in a defined range of ambient and atmospheric conditions. – Data obtained from remote sensing devices should be analyzed within defined limits of application.

@TechReport{Clifton2013,
Title = {IEA Wind RP 15. Ground-based vertically-profyling remote sensing for wind resource assessment},
Author = {A. Clifton and D. Elliott and M. Courtney.},
Institution = {IEA wind},
Year = {2013},
Number = {RP 15},
Abstract = {Vertically-profiling wind remote sensing technologies such as lidar and sodar potentially allow the collection of wind speed and direction data at heights up to typical wind turbine hubs, and beyond. Traditionally, wind data have been collected using cup anemometers and vanes on meteorological towers. It is therefore necessary to compare and verify the performance of sodar and lidar to these traditional traceable technologies and standards before using the data for a wind resource assessment. Care also needs to be taken that the remote sensing device is correctly installed, operated and maintained. This document includes recommended practices for the characterization, verification, installation, operation and maintenance, and data analysis of a remote sensing device for the purposes of wind energy assessments. Recommended practices for vertically-profiling lidar and sodar are often identical,
and so only recommended practices that differ between the two technologies refer to a specific technology. The following is a broad summary of the specific recommended practices in this document:
- Remote sensing device technologies, techniques and methods should be clearly characterized by manufacturers.
- Remote sensing devices should be verified periodically against calibrated reference devices that can be traced back to a national standard.
- Remote sensing devices should be installed by trained personnel following an installation checklist.
- The use of the remote sensing device should be documented, including verification activities, site deployments, servicing and data analysis.
- Remote sensing devices should be able to operate reliably and accurately in a defined range of ambient and atmospheric conditions.
- Data obtained from remote sensing devices should be analyzed within defined limits of application.},
Url = {http://www.ieawind.org/index_page_postings/RP/RP%2015_RemoteSensing_1stEd_8March2013.pdf}
}

• F. Dunne and L. Y. Pao, “Benefit of wind turbine preview control as a function of measurement coherence and preview time,” in Proceedings of the american control conference, Washington, DC, USA, 2013. doi:10.1109/ACC.2013.6579910
Wind turbine control is typically feedback only, relying on measurements from a generator-speed sensor, and sometimes strain gauges and accelerometers. Recently, lidar and other technologies that provide a preview measurement of the incoming wind speed are also becoming available. This raises a question: How much benefit do these preview measurements provide? In this paper, we focus on answering this question for wind turbine collective blade pitch control in above-rated wind speeds, with the objective of minimizing a cost function that includes both generator-speed error and blade pitch actuation. We assume the International Electrotechnical Commission (IEC) Kaimal wind spectrum and the National Renewable Energy Laboratory (NREL) 5-MW turbine model, linearized at various operating points (wind speeds). We then use ℌ2 synthesis to design an optimal combined feedforward/feedback controller that depends on both the amount of preview time available in the wind speed measurement, and the coherence between the wind measurement and the wind that is actually felt by the turbine. Finally, we show how the resulting closed-loop cost decreases as a function of measurement quality (coherence bandwidth) and preview time.We include the special case where measurement coherence equals zero, which is equivalent to no feedforward (feedback-only) control. Results show the benefit of wind turbine preview control as a function of measurement coherence and preview time.

@InProceedings{Dunne2013,
Title = {Benefit of wind turbine preview control as a function of measurement coherence and preview time},
Author = {Fiona Dunne and Lucy Y Pao},
Booktitle = {Proceedings of the American Control Conference},
Year = {2013},
Abstract = {Wind turbine control is typically feedback only, relying on measurements from a generator-speed sensor, and sometimes strain gauges and accelerometers. Recently, lidar and other technologies that provide a preview measurement of the incoming wind speed are also becoming available. This raises a question: How much benefit do these preview measurements provide? In this paper, we focus on answering this question for wind turbine collective blade pitch control in above-rated wind speeds, with the objective of minimizing a cost function that includes both generator-speed error and blade pitch actuation. We assume the International Electrotechnical Commission (IEC) Kaimal wind spectrum and the National Renewable Energy Laboratory (NREL) 5-MW turbine model, linearized at various operating points (wind speeds). We then use ℌ2 synthesis to design an optimal combined feedforward/feedback controller that depends on both the amount of preview time available in the wind speed measurement, and the coherence between the wind measurement and the wind that is actually felt by the turbine. Finally, we show how the resulting closed-loop cost decreases as a function of measurement quality (coherence bandwidth) and preview time.We include the special case where measurement coherence equals zero, which is equivalent to no feedforward (feedback-only) control. Results show the benefit of wind turbine preview control as a function of measurement coherence and preview time.},
Doi = {10.1109/ACC.2013.6579910},
Url = {http://dx.doi.org/10.1109/ACC.2013.6579910}
}

• I. Elorza, M. Iribas, and E. Miranda, “On the feasibility and limits of extreme load reduction for wind turbines via advanced sensing: a lidar case study,” in Proceedings of the american control conference, Washington, DC, USA, 2013. doi:10.1109/ACC.2013.6580038
This paper discusses some strategies that may be used to reduce extreme loads suffered by a wind turbine, by means of specific control actions based on wind speed measurements taken with a light detection and ranging device. Comparative results for a fully detailed wind turbine model are presented, in which the effectiveness of these strategies is demonstrated for the entire set of ultimate design load cases specified by the IEC61400-1 Ed. 2 standard.

@InProceedings{Elorza2013,
Title = {On the feasibility and limits of extreme load reduction for wind turbines via advanced sensing: A LIDAR case study},
Author = {Iker Elorza and Mikel Iribas and Edurne Miranda},
Booktitle = {Proceedings of the American Control Conference},
Year = {2013},
Abstract = {This paper discusses some strategies that may be used to reduce extreme loads suffered by a wind turbine, by means of specific control actions based on wind speed measurements taken with a light detection and ranging device. Comparative results for a fully detailed wind turbine model are presented, in which the effectiveness of these strategies is demonstrated for the entire set of ultimate design load cases specified by the IEC61400-1 Ed. 2 standard.},
Doi = {10.1109/ACC.2013.6580038},
Url = {http://dx.doi.org/10.1109/ACC.2013.6580038}
}

• B. D. Hirth and J. L. Schroeder, “Documenting wind speed and power deficits behind a utility-scale wind turbine,” Journal of applied meteorology and climatology, vol. 52, iss. 1, pp. 39-46, 2013. doi:10.1175/JAMC-D-12-0145.1
High-spatial-and-temporal-resolution radial velocity measurements surrounding a single utility-scale wind turbine were collected using the Texas Tech University Ka-band mobile research radars. The measurements were synthesized to construct the first known dual-Doppler analyses of the mean structure and variability of a single turbine wake. The observations revealed a wake length that subjectively exceeded 20 rotor diameters, which far exceeds the typically employed turbine spacing of 7–10 rotor diameters. The mean horizontal wind speed deficits found within the turbine wake region relative to the free streamflow were related to potential reductions in the available power for a downwind turbine. Mean wind speed reductions of 17.4\% (14.8\%) were found at 7 (10) rotor diameters downwind, corresponding to a potential power output reduction of 43.6\% (38.2\%). The wind speed deficits found within the wake also exhibit large variability over short time intervals; this variability would have an appreciable impact on the inflow of a downstream turbine. The full understanding and application of these newly collected data have the potential to alter current wind-farm design and layout practices and to affect the cost of energy.

@Article{Hirth2013,
Title = {Documenting Wind Speed and Power Deficits behind a Utility-Scale Wind Turbine},
Author = {Brian D. Hirth and John L. Schroeder},
Journal = {Journal of Applied Meteorology and Climatology},
Year = {2013},
Number = {1},
Pages = {39-46},
Volume = {52},
Abstract = {High-spatial-and-temporal-resolution radial velocity measurements surrounding a single utility-scale wind turbine were collected using the Texas Tech University Ka-band mobile research radars. The measurements were synthesized to construct the first known dual-Doppler analyses of the mean structure and variability of a single turbine wake. The observations revealed a wake length that subjectively exceeded 20 rotor diameters, which far exceeds the typically employed turbine spacing of 7–10 rotor diameters. The mean horizontal wind speed deficits found within the turbine wake region relative to the free streamflow were related to potential reductions in the available power for a downwind turbine. Mean wind speed reductions of 17.4\% (14.8\%) were found at 7 (10) rotor diameters downwind, corresponding to a potential power output reduction of 43.6\% (38.2\%). The wind speed deficits found within the wake also exhibit large variability over short time intervals; this variability would have an appreciable impact on the inflow of a downstream turbine. The full understanding and application of these newly collected data have the potential to alter current wind-farm design and layout practices and to affect the cost of energy.},
Doi = {10.1175/JAMC-D-12-0145.1},
Url = {http://dx.doi.org/10.1175/JAMC-D-12-0145.1}
}

• G. V. Iungo, Y. Wu, and F. Porté-Agel, “Field measurements of wind turbine wakes with lidars,” Journal of atmospheric and oceanic technology, vol. 30, iss. 2, pp. 274-287, 2013. doi:10.1175/JTECH-D-12-00051.1
Field measurements of the wake flow produced from a 2-MW Enercon E-70 wind turbine were performed using three scanning Doppler wind lidars. A GPS-based technique was used to determine the position of the wind turbine and the wind lidar locations, as well as the direction of the laser beams. The lidars used in this study are characterized by a high spatial resolution of 18 m, which allows the detailed characterization of the wind turbine wake. Two-dimensional measurements of wind speed were carried out by scanning a single lidar over the vertical symmetry plane of the wake. The mean axial velocity field was then retrieved by averaging 2D scans performed consecutively. To investigate wake turbulence, single lidar measurements were performed by staring the laser beam at fixed directions and using the maximum sampling frequency. From these tests, peaks in the velocity variance are detected within the wake in correspondence of the turbine top tip height; this enhanced turbulence could represent a source of dangerous fatigue loads for downstream turbines. The spectral density of the measured velocity fluctuations shows a clear inertial-range scaling behavior. Then, simultaneous measurements with two lidars were performed in order to characterize both the axial and the vertical velocity components. For this setup, the two velocity components were retrieved only for measurement points for which the two laser beams crossed nearly at a right angle. Statistics were computed over the sample set for both velocity components, and they showed strong flow fluctuations in the near-wake region at turbine top tip height, with a turbulence intensity of about 30%.

@Article{Iungo2012,
Title = {Field Measurements of Wind Turbine Wakes with Lidars},
Author = {Iungo, Giacomo Valerio and Wu, Yu-Ting and Porté-Agel, Fernando},
Journal = {Journal of Atmospheric and Oceanic Technology},
Year = {2013},
Number = {2},
Pages = {274--287},
Volume = {30},
Abstract = {Field measurements of the wake flow produced from a 2-MW Enercon E-70 wind turbine were performed using three scanning Doppler wind lidars. A GPS-based technique was used to determine the position of the wind turbine and the wind lidar locations, as well as the direction of the laser beams. The lidars used in this study are characterized by a high spatial resolution of 18 m, which allows the detailed characterization of the wind turbine wake. Two-dimensional measurements of wind speed were carried out by scanning a single lidar over the vertical symmetry plane of the wake. The mean axial velocity field was then retrieved by averaging 2D scans performed consecutively. To investigate wake turbulence, single lidar measurements were performed by staring the laser beam at fixed directions and using the maximum sampling frequency. From these tests, peaks in the velocity variance are detected within the wake in correspondence of the turbine top tip height; this enhanced turbulence could represent a source of dangerous fatigue loads for downstream turbines. The spectral density of the measured velocity fluctuations shows a clear inertial-range scaling behavior. Then, simultaneous measurements with two lidars were performed in order to characterize both the axial and the vertical velocity components. For this setup, the two velocity components were retrieved only for measurement points for which the two laser beams crossed nearly at a right angle. Statistics were computed over the sample set for both velocity components, and they showed strong flow fluctuations in the near-wake region at turbine top tip height, with a turbulence intensity of about 30%.},
Doi = {10.1175/JTECH-D-12-00051.1},
Url = {http://dx.doi.org/10.1175/JTECH-D-12-00051.1}
}

• K. A. Kragh, M. H. Hansen, and T. Mikkelsen, “Precision and shortcomings of yaw error estimation using spinner-based light detection and ranging,” Wind energy, vol. 16, iss. 3, pp. 353-366, 2013. doi:10.1002/we.1492
When extracting energy from the wind using horizontal axis wind turbines, the ability to align the rotor axis with the mean wind direction is crucial. In previous work, a method for estimating the yaw error based on measurements from a spinner mounted light detection and ranging (LIDAR) device was developed and tested. In this study, the simulation parameter space is extended to include higher levels of turbulence intensity. Furthermore, the method is applied to experimental data and compared with met-mast data corrected for a calibration error that was not discovered during previous work. Finally, the shortcomings of using a spinner mounted LIDAR for yaw error estimation are discussed. The extended simulation study shows that with the applied method, the yaw error can be estimated with a precision of a few degrees, even in highly turbulent flows. Applying the method to experimental data reveals an average yaw error of approximately 9° during a period of 2 h, and good correlation is seen between LIDAR-based estimates and met-mast data. The final discussion suggests a number of challenges of the method when applied to measurements in complex flow.

@Article{Kragh2013,
Title = {Precision and shortcomings of yaw error estimation using spinner-based light detection and ranging},
Author = {Kragh, Knud A. and Hansen, Morten H. and Mikkelsen, Torben},
Journal = {Wind Energy},
Year = {2013},
Number = {3},
Pages = {353--366},
Volume = {16},
Abstract = {When extracting energy from the wind using horizontal axis wind turbines, the ability to align the rotor axis with the mean wind direction is crucial. In previous work, a method for estimating the yaw error based on measurements from a spinner mounted light detection and ranging (LIDAR) device was developed and tested. In this study, the simulation parameter space is extended to include higher levels of turbulence intensity. Furthermore, the method is applied to experimental data and compared with met-mast data corrected for a calibration error that was not discovered during previous work. Finally, the shortcomings of using a spinner mounted LIDAR for yaw error estimation are discussed. The extended simulation study shows that with the applied method, the yaw error can be estimated with a precision of a few degrees, even in highly turbulent flows. Applying the method to experimental data reveals an average yaw error of approximately 9° during a period of 2 h, and good correlation is seen between LIDAR-based estimates and met-mast data. The final discussion suggests a number of challenges of the method when applied to measurements in complex flow.},
Doi = {10.1002/we.1492},
Url = {http://dx.doi.org/10.1002/we.1492}
}

• M. Kristalny, D. Madjidian, and T. Knudsen, “On using wind speed preview to reduce wind turbine tower oscillations,” Control systems technology, ieee transactions on, vol. 21, iss. 4, pp. 1191-1198, 2013. doi:10.1109/TCST.2013.2261070
We investigate the potential of using previewed wind speed measurements for damping wind turbine fore-aft tower oscillations. Using recent results on continuous-time H2 preview control, we develop a numerically efficient framework for the feedforward controller synthesis. One of the major benefits of the proposed framework is that it allows us to account for measurement distortion. This results in a controller that is tailored to the quality of the previewed data. A simple yet meaningful parametric model of the measurement distortion is proposed and used to analyze the effects of distortion characteristics on the achievable performance and on the required length of preview. We demonstrate the importance of accounting for the distortion in the controller synthesis and quantify the potential benefits of using previewed information by means of simulations based on real-world turbine data.

@Article{Kristalny2013,
Title = {On Using Wind Speed Preview to Reduce Wind Turbine Tower Oscillations},
Author = {Kristalny, M. and Madjidian, D. and Knudsen, T.},
Journal = {Control Systems Technology, IEEE Transactions on},
Year = {2013},
Number = {4},
Pages = {1191-1198},
Volume = {21},
Abstract = {We investigate the potential of using previewed wind speed measurements for damping wind turbine fore-aft tower oscillations. Using recent results on continuous-time H2 preview control, we develop a numerically efficient framework for the feedforward controller synthesis. One of the major benefits of the proposed framework is that it allows us to account for measurement distortion. This results in a controller that is tailored to the quality of the previewed data. A simple yet meaningful parametric model of the measurement distortion is proposed and used to analyze the effects of distortion characteristics on the achievable performance and on the required length of preview. We demonstrate the importance of accounting for the distortion in the controller synthesis and quantify the potential benefits of using previewed information by means of simulations based on real-world turbine data.},
Doi = {10.1109/TCST.2013.2261070},
Url = {http://dx.doi.org/10.1109/TCST.2013.2261070}
}

• J. Laks, E. Simley, and L. Y. Pao, “A spectral model for evaluating the effect of wind evolution on wind turbine preview control,” in Proceedings of the american control conference, Washington, DC, USA, 2013. doi:10.1109/ACC.2013.6580400
As wind turbines become larger and more flexible, the potential benefits of load mitigating control systems become more important to reduce fatigue and extend component life. In the last five years, there has been significant research activity exploring the effectiveness of preview control techniques that may be feasible using advanced wind measurement technologies like LIDAR (light detection and ranging). However, most control development tools use Taylor’s frozen turbulence hypothesis. The end result is that preview measurements made up-stream from the rotor can be obtained with unrealistic accuracy, because the same wind velocities eventually arrive at the turbine. In this study, we extend the spectral methods commonly used to generate turbulent wind fields for controls simulation, but in a way that emulates wind evolution. This changes preview measurements made upwind from the rotor, in such a way that the differences- between the preview measurements and speeds arriving at the turbine- increase with distance from the rotor. We then evaluate the degradation in load mitigation performance of a controller that uses preview measurements obtained at various distances in front of the rotor.

@InProceedings{Laks2013,
Title = {A Spectral Model for Evaluating the Effect of Wind Evolution on Wind Turbine Preview Control},
Author = {Laks, Jason and Simley, Eric and Pao, Lucy Y},
Booktitle = {Proceedings of the American Control Conference},
Year = {2013},
Abstract = {As wind turbines become larger and more flexible, the potential benefits of load mitigating control systems become more important to reduce fatigue and extend component life. In the last five years, there has been significant research activity exploring the effectiveness of preview control techniques that may be feasible using advanced wind measurement technologies like LIDAR (light detection and ranging). However, most control development tools use Taylor's frozen turbulence hypothesis. The end result is that preview measurements made up-stream from the rotor can be obtained with unrealistic accuracy, because the same wind velocities eventually arrive at the turbine. In this study, we extend the spectral methods commonly used to generate turbulent wind fields for controls simulation, but in a way that emulates wind evolution. This changes preview measurements made upwind from the rotor, in such a way that the differences- between the preview measurements and speeds arriving at the turbine- increase with distance from the rotor. We then evaluate the degradation in load mitigation performance of a controller that uses preview measurements obtained at various distances in front of the rotor.},
Doi = {10.1109/ACC.2013.6580400},
Url = {http://dx.doi.org/10.1109/ACC.2013.6580400}
}

• T. Mikkelsen, N. Angelou, K. Hansen, M. Sjöholm, M. Harris, C. Sliner, P. Hadley, R. Scullion, G. Ellis, and G. Vives, “A spinner-integrated wind lidar for enhanced wind turbine control,” Wind energy, vol. 16, iss. 4, pp. 625-643, 2013. doi:10.1002/we.1564
A field test with a continuous wave wind lidar (ZephIR) installed in the rotating spinner of a wind turbine for unimpeded preview measurements of the upwind approaching wind conditions is described. The experimental setup with the wind lidar on the tip of the rotating spinner of a large 80 m rotor diameter, 59 m hub height 2.3 MW wind turbine (Vestas NM80), located at Tjæreborg Enge in western Denmark is presented. Preview wind data at two selected upwind measurement distances, acquired during two measurement periods of different wind speed and atmospheric stability conditions, are analyzed. The lidar-measured speed, shear and direction of the wind field previewed in front of the turbine are compared with reference measurements from an adjacent met mast and also with the speed and direction measurements on top of the nacelle behind the rotor plane used by the wind turbine itself. Yaw alignment of the wind turbine based on the spinner lidar measurements is compared with wind direction measurements from both the nearby reference met mast and the turbine’s own yaw alignment wind vane. Furthermore, the ability to detect vertical wind shear and vertical direction veer in the inflow, through the analysis of the spinner lidar data, is investigated. Finally, the potential for enhancing turbine control and performance based on wind lidar preview measurements in combination with feed-forward enabled turbine controllers is discussed.

@Article{Mikkelsen2013,
Title = {A spinner-integrated wind lidar for enhanced wind turbine control},
Author = {Mikkelsen, T. and Angelou, N. and Hansen, K. and Sjöholm, M. and Harris, M. and Sliner, C. and Hadley, P. and Scullion, R. and Ellis, G. and Vives, G.},
Journal = {Wind Energy},
Year = {2013},
Number = {4},
Pages = {625--643},
Volume = {16},
Abstract = {A field test with a continuous wave wind lidar (ZephIR) installed in the rotating spinner of a wind turbine for unimpeded preview measurements of the upwind approaching wind conditions is described. The experimental setup with the wind lidar on the tip of the rotating spinner of a large 80 m rotor diameter, 59 m hub height 2.3 MW wind turbine (Vestas NM80), located at Tjæreborg Enge in western Denmark is presented. Preview wind data at two selected upwind measurement distances, acquired during two measurement periods of different wind speed and atmospheric stability conditions, are analyzed. The lidar-measured speed, shear and direction of the wind field previewed in front of the turbine are compared with reference measurements from an adjacent met mast and also with the speed and direction measurements on top of the nacelle behind the rotor plane used by the wind turbine itself. Yaw alignment of the wind turbine based on the spinner lidar measurements is compared with wind direction measurements from both the nearby reference met mast and the turbine's own yaw alignment wind vane. Furthermore, the ability to detect vertical wind shear and vertical direction veer in the inflow, through the analysis of the spinner lidar data, is investigated. Finally, the potential for enhancing turbine control and performance based on wind lidar preview measurements in combination with feed-forward enabled turbine controllers is discussed.},
Doi = {10.1002/we.1564},
Url = {http://dx.doi.org/10.1002/we.1564}
}

• M. Mirzaei, M. Soltani, N. K. Poulsen, and H. H. Niemann, “Model predictive control of wind turbines using uncertain lidar measurements,” in Proceedings of the american control conference, Washington, DC, USA, 2013. doi:10.1109/ACC.2013.6580167
The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined by the effective wind speed on the rotor disc. We take the wind speed as a scheduling variable. The wind speed is measurable ahead of the turbine using LIDARs, therefore, the scheduling variable is known for the entire prediction horizon. By taking the advantage of having future values of the scheduling variable, we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point based on wind speed estimation are given. It is shown that with appropriate signal processing techniques, LIDAR measurements improve the performance of the wind turbine controller.

@InProceedings{Mirzaei2013,
Title = {Model predictive control of wind turbines using uncertain LIDAR measurements},
Author = {Mirzaei, M. and Soltani, M. and Poulsen, N.K. and Niemann, H.H.},
Booktitle = {Proceedings of the American Control Conference},
Year = {2013},
Abstract = {The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined by the effective wind speed on the rotor disc. We take the wind speed as a scheduling variable. The wind speed is measurable ahead of the turbine using LIDARs, therefore, the scheduling variable is known for the entire prediction horizon. By taking the advantage of having future values of the scheduling variable, we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point based on wind speed estimation are given. It is shown that with appropriate signal processing techniques, LIDAR measurements improve the performance of the wind turbine controller.},
Doi = {10.1109/ACC.2013.6580167},
Url = {http://dx.doi.org/10.1109/ACC.2013.6580167}
}

• A. Peña, C. B. Hasager, J. Lange, J. Anger, M. Badger, F. Bingöl, O. Bischoff, J. Cariou, F. Dunne, S. Emeis, M. Harris, M. Hofsäss, I. Karagali, J. Laks, S. E. Larsen, J. Mann, T. Mikkelsen, L. Y. Pao, M. Pitter, A. Rettenmeier, A. Sathe, F. Scanzani, D. Schlipf, E. Simley, C. Slinger, R. Wagner, and I. Würth, “Remote sensing for wind energy,” DTU Wind Energy, E-Report 0029(EN), 2013.
The Remote Sensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risø) during the first PhD Summer School: Remote Sensing in Wind Energy. Thus it is closely linked to the PhD Summer Schools where state-of-the-art is presented during the lecture sessions. The advantage of the report is to supplement with in-depth, article style information. Thus we strive to provide link from the lectures, field demonstrations, and hands-on exercises to theory. The report will allow alumni to trace back details after the course and benefit from the collection of information. This is the third edition of the report (first externally available), after very successful and demanded first two, and we warmly acknowledge all the contributing authors for their work in the writing of the chapters, and we also acknowledge all our colleagues in the Meteorology and Test and Measurements Sections from DTU Wind Energy in the PhD Summer Schools. We hope to continue adding more topics in future editions and to update and improve as necessary, to provide a truly state-of-the-art ‘guideline’ available for people involved in Remote Sensing in Wind Energy.

@TechReport{Pena2013,
Title = {Remote Sensing for Wind Energy},
Author = {Alfredo Peña and Hasager, Charlotte Bay and Julia Lange and Jan Anger and Merete Badger and Ferhat Bingöl and Oliver Bischoff and Jean-Pierre Cariou and Fiona Dunne and Stefan Emeis and Michael Harris and Martin Hofsäss and Ioanna Karagali and Jason Laks and Larsen, Søren Ejling and Jakob Mann and Torben Mikkelsen and Pao, Lucy Y. and Mark Pitter and Andreas Rettenmeier and Ameya Sathe and Fabio Scanzani and David Schlipf and Eric Simley and Chris Slinger and Rozenn Wagner and Ines Würth},
Institution = {DTU Wind Energy},
Year = {2013},
Number = {0029(EN)},
Type = {E-Report},
Abstract = {The Remote Sensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risø) during the first PhD Summer School: Remote Sensing in Wind Energy. Thus it is closely linked to the PhD Summer Schools where state-of-the-art is presented during the lecture sessions. The advantage of the report is to supplement with in-depth, article style information. Thus we strive to provide link from the lectures, field demonstrations, and hands-on exercises to theory. The report will allow alumni to trace back details after the course and benefit from the collection of information. This is the third edition of the report (first externally available), after very successful and demanded first two, and we warmly acknowledge all the contributing authors for their work in the writing of the chapters, and we also acknowledge all our colleagues in the Meteorology and Test and Measurements Sections from DTU Wind Energy in the PhD Summer Schools. We hope to continue adding more topics in future editions and to update and improve as necessary, to provide a truly
state-of-the-art ‘guideline’ available for people involved in Remote Sensing in Wind Energy.},
Url = {http://orbit.dtu.dk/en/publications/remote-sensing-for-wind-energy(132f5767-713f-4f86-b437-ea0466717924).html}
}

• M. E. Rhodes and J. K. Lundquist, “The effect of wind-turbine wakes on summertime us midwest atmospheric wind profiles as observed with ground-based doppler lidar,” Boundary-layer meteorology, vol. 149, iss. 1, pp. 85-103, 2013. doi:10.1007/s10546-013-9834-x
We examine the influence of a modern multi-megawatt wind turbine on wind and turbulence profiles three rotor diameters (D) downwind of the turbine. Light detection and ranging (lidar) wind-profile observations were collected during summer 2011 in an operating wind farm in central Iowa at 20-m vertical intervals from 40 to 220 m above the surface. After a calibration period during which two lidars were operated next to each other, one lidar was located approximately 2D directly south of a wind turbine; the other lidar was moved approximately 3D north of the same wind turbine. Data from the two lidars during southerly flow conditions enabled the simultaneous capture of inflow and wake conditions. The inflow wind and turbulence profiles exhibit strong variability with atmospheric stability: daytime profiles are well-mixed with little shear and strong turbulence, while nighttime profiles exhibit minimal turbulence and considerable shear across the rotor disk region and above. Consistent with the observations available from other studies and with wind-tunnel and large-eddy simulation studies, measurable reductions in wake wind-speeds occur at heights spanning the wind turbine rotor (43–117 m), and turbulent quantities increase in the wake. In generalizing these results as a function of inflow wind speed, we find the wind-speed deficit in the wake is largest at hub height or just above, and the maximum deficit occurs when wind speeds are below the rated speed for the turbine. Similarly, the maximum enhancement of turbulence kinetic energy and turbulence intensity occurs at hub height, although observations at the top of the rotor disk do not allow assessment of turbulence in that region. The wind shear below turbine hub height (quantified here with the power-law coefficient) is found to be a useful parameter to identify whether a downwind lidar observes turbine wake or free-flow conditions. These field observations provide data for validating turbine-wake models and wind-tunnel observations, and for guiding assessments of the impacts of wakes on surface turbulent fluxes or surface temperatures downwind of turbines.

@Article{Rhodes2013,
Title = {The Effect of Wind-Turbine Wakes on Summertime US Midwest Atmospheric Wind Profiles as Observed with Ground-Based Doppler Lidar},
Author = {Rhodes, Michael E. and Lundquist, Julie K.},
Journal = {Boundary-Layer Meteorology},
Year = {2013},
Number = {1},
Pages = {85--103},
Volume = {149},
Abstract = {We examine the influence of a modern multi-megawatt wind turbine on wind and turbulence profiles three rotor diameters (D) downwind of the turbine. Light detection and ranging (lidar) wind-profile observations were collected during summer 2011 in an operating wind farm in central Iowa at 20-m vertical intervals from 40 to 220 m above the surface. After a calibration period during which two lidars were operated next to each other, one lidar was located approximately 2D directly south of a wind turbine; the other lidar was moved approximately 3D north of the same wind turbine. Data from the two lidars during southerly flow conditions enabled the simultaneous capture of inflow and wake conditions. The inflow wind and turbulence profiles exhibit strong variability with atmospheric stability: daytime profiles are well-mixed with little shear and strong turbulence, while nighttime profiles exhibit minimal turbulence and considerable shear across the rotor disk region and above. Consistent with the observations available from other studies and with wind-tunnel and large-eddy simulation studies, measurable reductions in wake wind-speeds occur at heights spanning the wind turbine rotor (43–117 m), and turbulent quantities increase in the wake. In generalizing these results as a function of inflow wind speed, we find the wind-speed deficit in the wake is largest at hub height or just above, and the maximum deficit occurs when wind speeds are below the rated speed for the turbine. Similarly, the maximum enhancement of turbulence kinetic energy and turbulence intensity occurs at hub height, although observations at the top of the rotor disk do not allow assessment of turbulence in that region. The wind shear below turbine hub height (quantified here with the power-law coefficient) is found to be a useful parameter to identify whether a downwind lidar observes turbine wake or free-flow conditions. These field observations provide data for validating turbine-wake models and wind-tunnel observations, and for guiding assessments of the impacts of wakes on surface turbulent fluxes or surface temperatures downwind of turbines.},
Doi = {10.1007/s10546-013-9834-x},
Url = {http://dx.doi.org/10.1007/s10546-013-9834-x}
}

• A. Sathe and J. Mann, “A review of turbulence measurements using ground-based wind lidars,” Atmospheric measurement techniques, vol. 6, iss. 11, pp. 3147-3167, 2013. doi:10.5194/amt-6-3147-2013
A review of turbulence measurements using ground-based wind lidars is carried out. Works performed in the last 30 yr, i.e., from 1972–2012 are analyzed. More than 80% of the work has been carried out in the last 15 yr, i.e., from 1997–2012. New algorithms to process the raw lidar data were pioneered in the first 15 yr, i.e., from 1972–1997, when standard techniques could not be used to measure turbulence. Obtaining unfiltered turbulence statistics from the large probe volume of the lidars has been and still remains the most challenging aspect. Until now, most of the processing algorithms that have been developed have shown that by combining an isotropic turbulence model with raw lidar measurements, we can obtain unfiltered statistics. We believe that an anisotropic turbulence model will provide a more realistic measure of turbulence statistics. Future development in algorithms will depend on whether the unfiltered statistics can be obtained without the aid of any turbulence model. With the tremendous growth of the wind energy sector, we expect that lidars will be used for turbulence measurements much more than ever before.

@Article{Sathe2013b,
Title = {A review of turbulence measurements using ground-based wind lidars},
Author = {A. Sathe and J. Mann},
Journal = {Atmospheric Measurement Techniques},
Year = {2013},
Number = {11},
Pages = {3147--3167},
Volume = {6},
Abstract = {A review of turbulence measurements using ground-based wind lidars is carried out. Works performed in the last 30 yr, i.e., from 1972–2012 are analyzed. More than 80% of the work has been carried out in the last 15 yr, i.e., from 1997–2012. New algorithms to process the raw lidar data were pioneered in the first 15 yr, i.e., from 1972–1997, when standard techniques could not be used to measure turbulence. Obtaining unfiltered turbulence statistics from the large probe volume of the lidars has been and still remains the most challenging aspect. Until now, most of the processing algorithms that have been developed have shown that by combining an isotropic turbulence model with raw lidar measurements, we can obtain unfiltered statistics. We believe that an anisotropic turbulence model will provide a more realistic measure of turbulence statistics. Future development in algorithms will depend on whether the unfiltered statistics can be obtained without the aid of any turbulence model. With the tremendous growth of the wind energy sector, we expect that lidars will be used for turbulence measurements much more than ever before.},
Doi = {10.5194/amt-6-3147-2013},
Url = {http://dx.doi.org/10.5194/amt-6-3147-2013}
}

• D. Schlipf, D. J. Schlipf, and M. Kühn, “Nonlinear model predictive control of wind turbines using LIDAR,” Wind energy, vol. 16, iss. 7, pp. 1107-1129, 2013. doi:10.1002/we.1533
LIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts in wind energy such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller with a baseline controller, showing the advantages of using the wind predictions in the optimization problem to reduce wind turbine extreme and fatigue loads on tower and blades as well as to limit the pitch rates. The wind information is obtained by a detailed simulation of a LIDAR system. The controller design is evaluated and tested in a simulation environment with coherent gusts and a set of turbulent wind fields using a detailed aeroelastic model of the wind turbine over the full operation region. Results show promising load reduction up to 50% for extreme gusts and 30% for lifetime fatigue loads without negative impact on overall energy production. This controller can be considered as an upper bound for other LIDAR assisted controllers that are more suited for real time applications.

@Article{Schlipf2013e,
Title = {Nonlinear Model Predictive Control of Wind Turbines Using {LIDAR}},
Author = {David Schlipf and Dominik Johannes Schlipf and Martin Kühn},
Journal = {Wind Energy},
Year = {2013},
Number = {7},
Pages = {1107-1129},
Volume = {16},
Abstract = {LIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts in wind energy such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller with a baseline controller, showing the advantages of using the wind predictions in the optimization problem to reduce wind turbine extreme and fatigue loads on tower and blades as well as to limit the pitch rates. The wind information is obtained by a detailed simulation of a LIDAR system. The controller design is evaluated and tested in a simulation environment with coherent gusts and a set of turbulent wind fields using a detailed aeroelastic model of the wind turbine over the full operation region. Results show promising load reduction up to 50% for extreme gusts and 30% for lifetime fatigue loads without negative impact on overall energy production. This controller can be considered as an upper bound for other LIDAR assisted controllers that are more suited for real time applications.},
Doi = {10.1002/we.1533},
Url = {http://dx.doi.org/10.1002/we.1533}
}

• D. Schlipf, J. Mann, and P. W. Cheng, “Model of the correlation between lidar systems and wind turbines for lidar assisted control,” Journal of atmospheric and oceanic technology, vol. 30, iss. 10, pp. 2233-2240, 2013. doi:10.1175/JTECH-D-13-00077.1
Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system, different rotor sizes, and wind evolution. The method is compared to real measurement data with promising results. In addition, examples depict how this model can be used to design an optimal controller and how the configuration of a lidar system is optimized for a given turbine to improve the correlation

@Article{Schlipf2013a,
Title = {Model of the Correlation between Lidar Systems and Wind Turbines for Lidar Assisted Control},
Author = {David Schlipf and Jakob Mann and Po Wen Cheng},
Journal = {Journal of Atmospheric and Oceanic Technology},
Year = {2013},
Number = {10},
Pages = {2233-2240},
Volume = {30},
Abstract = {Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system, different rotor sizes, and wind evolution. The method is compared to real measurement data with promising results. In addition, examples depict how this model can be used to design an optimal controller and how the configuration of a lidar system is optimized for a given turbine to improve the correlation},
Doi = {10.1175/JTECH-D-13-00077.1},
Url = {http://dx.doi.org/10.1175/JTECH-D-13-00077.1}
}

• D. Schlipf, P. Fleming, S. Kapp, A. Scholbrock, F. Haizmann, F. Belen, A. Wright, and P. W. Cheng, “Direct speed control using lidar and turbine data,” in Proceedings of the american control conference, Washington, DC, USA, 2013. doi:10.1109/ACC.2013.6580163
LIDAR systems are able to provide preview information of the wind speed in front of wind turbines. One proposed use of this information is to increase the energy capture of the turbine by adjusting the rotor speed directly to maintain operation at the optimal tip-speed ratio, a technique referred to as Direct Speed Control (DSC). Previous work has indicated that for large turbines the marginal benefit of the direct speed controller in terms of increased power does not compensate for the increase of the shaft loads. However, the technique has not yet been adequately tested to make this determination conclusively. Further, it is possible that applying DSC to smaller turbines could be worthwhile because of the higher rotor speed fluctuations and the small rotor inertia. This paper extends the previous work on direct speed controllers. A DSC is developed for a 600 kW experimental turbine and is evaluated theoretically and in simulation. Because the actual turbine has a mounted LIDAR, data collected from the turbine and LIDAR during operation are used to perform a hybrid simulation. This technique allows a realistic simulation to be performed, which provides good agreement with theoretical predictions.

@InProceedings{Schlipf2013c,
Title = {Direct Speed Control Using LIDAR and Turbine Data},
Author = {Schlipf, David and Fleming, Paul and Kapp, Stefan and Scholbrock, Andrew and Haizmann, Florian and Belen, Fred and Wright, Alan and Cheng, Po Wen},
Booktitle = {Proceedings of the American Control Conference},
Year = {2013},
Abstract = {LIDAR systems are able to provide preview information of the wind speed in front of wind turbines. One proposed use of this information is to increase the energy capture of the turbine by adjusting the rotor speed directly to maintain operation at the optimal tip-speed ratio, a technique referred to as Direct Speed Control (DSC). Previous work has indicated that for large turbines the marginal benefit of the direct speed controller in terms of increased power does not compensate for the increase of the shaft loads. However, the technique has not yet been adequately tested to make this determination conclusively. Further, it is possible that applying DSC to smaller turbines could be worthwhile because of the higher rotor speed fluctuations and the small rotor inertia. This paper extends the previous work on direct speed controllers. A DSC is developed for a 600 kW experimental turbine and is evaluated theoretically and in simulation. Because the actual turbine has a mounted LIDAR, data collected from the turbine and LIDAR during operation are used to perform a hybrid simulation. This technique allows a realistic simulation to be performed, which provides good agreement with theoretical predictions.},
Doi = {10.1109/ACC.2013.6580163},
Url = {http://dx.doi.org/10.1109/ACC.2013.6580163}
}

• D. Schlipf and P. W. Cheng, “Adaptive feed forward control for wind turbines,” At – automatisierungstechnik, vol. 61, iss. 5, pp. 329-338, 2013. doi:10.1524/auto.2013.0029
This paper presents how LIDAR measurements can be used in a feed forward control to reduce rotor speed variation and loads on wind turbines. Core of this control strategy is an adaptive filter that takes into account the continuous changes in the prediction time and in the correlation between the turbine reaction and the preview. Results will be validated with measurement data of a 5 MW wind turbine.

@Article{Schlipf2013b,
Title = {Adaptive Feed Forward Control for Wind Turbines},
Author = {David Schlipf and Po Wen Cheng},
Journal = {at - Automatisierungstechnik},
Year = {2013},
Number = {5},
Pages = {329-338},
Volume = {61},
Abstract = {This paper presents how LIDAR measurements can be used in a feed forward control to reduce rotor speed variation and loads on wind turbines. Core of this control strategy is an adaptive filter that takes into account the continuous changes in the prediction time and in the correlation between the turbine reaction and the preview. Results will be validated with measurement data of a 5 MW wind turbine.},
Doi = {10.1524/auto.2013.0029},
Url = {http://dx.doi.org/10.1524/auto.2013.0029}
}

• A. Scholbrock, P. Fleming, A. Wright, C. Slinger, J. Medley, and M. Harris, “Field test results from lidar measured yaw control for improved yaw alignment with the NREL controls advanced research turbine,” in Proceedings of the 33rd wind energy symposium aiaa scitech conference, Kissimmee, USA, 2013. doi:10.2514/6.2015-1209
This paper describes field tests of a light detection and ranging (lidar) device placed forward looking on the nacelle of a wind turbine and used as a wind direction measurement to directly control the yaw position of a wind turbine. Conventionally, a wind turbine controls its yaw direction using a nacelle-mounted wind vane. If there is a bias in the measurement from the nacelle-mounted wind vane, a reduction in power production will be observed. This bias could be caused by a number of issues such as: poor calibration, electromagnetic interference, rotor wake, or other effects. With a lidar mounted on the nacelle, a measurement of the wind could be made upstream of the wind turbine where the wind is not being influenced by the rotor’s wake or induction zone. Field tests were conducted with the lidar measured yaw system and the nacelle wind vane measured yaw system. Results show that a lidar can be used to effectively measure the yaw error of the wind turbine, and for this experiment, they also showed an improvement in power capture because of reduced yaw misalignment when compared to the nacelle wind vane measured yaw system.

@InProceedings{Scholbrock2013a,
Title = {Field test results from lidar measured yaw control for improved yaw alignment with the {NREL} controls advanced research turbine},
Author = {A. Scholbrock and P. Fleming and A. Wright and C. Slinger and J. Medley and M. Harris},
Booktitle = {Proceedings of the 33rd Wind Energy Symposium AIAA SciTech Conference},
Year = {2013},
Abstract = {This paper describes field tests of a light detection and ranging (lidar) device placed forward looking on the nacelle of a wind turbine and used as a wind direction measurement to directly control the yaw position of a wind turbine. Conventionally, a wind turbine controls its yaw direction using a nacelle-mounted wind vane. If there is a bias in the measurement from the nacelle-mounted wind vane, a reduction in power production will be observed. This bias could be caused by a number of issues such as: poor calibration, electromagnetic interference, rotor wake, or other effects. With a lidar mounted on the nacelle, a measurement of the wind could be made upstream of the wind turbine where the wind is not being influenced by the rotor’s wake or induction zone. Field tests were conducted with the lidar measured yaw system and the nacelle wind vane measured yaw system. Results show that a lidar can be used to effectively measure the yaw error of the wind turbine, and for this experiment, they also showed an improvement in power capture because of reduced yaw misalignment when compared to the nacelle wind vane measured yaw system.},
Doi = {10.2514/6.2015-1209},
Url = {http://dx.doi.org/10.2514/6.2015-1209}
}

• A. Scholbrock, P. Fleming, L. Fingersh, A. Wright, D. Schlipf, F. Haizmann, and F. Belen, “Field testing LIDAR based feed-forward controls on the NREL controls advanced research turbine,” in Proceedings of the 51st aiaa aerospace sciences meeting including the new horizons forum and aerospace exposition, Dallas, USA, 2013. doi:10.2514/6.2013-818
Researchers at the National Renewable Energy Laboratory (NREL) and the University of Stuttgart are designing, implementing, and testing advanced feedback and feed-forward controls for multimegawatt wind turbines that will help reduce the cost of wind energy. Past wind turbine controllers have depended on turbine feedback measurements to determine the controller pitch commands. In this setup, wind speed disturbances can only be corrected after their effects have been detected in the turbine’s loads and dynamic response, which causes a delayed control response due to turbine and pitch actuator dynamics. LIght Detection And Ranging (LIDAR) systems can provide information regarding the approaching wind field to the controller in advance, thereby increasing the controller’s available reaction time and allowing pitch actuation to occur in advance to mitigate wind disturbance effects. Feed-forward control algorithms that use these “look ahead” wind speed measurements can improve load mitigation and controller performance compared to feedback only controllers. This paper describes the development and field testing of a feed-forward collective pitch control algorithm to show its effects on speed regulation in above-rated wind speeds. The controller is implemented and field tested on one of the Controls Advanced Research Turbines (CARTs) at NREL. The wind speed measurements to the feed-forward controller are provided by BlueScout Technologies’ Optical Control System (OCS) LIDAR mounted on the nacelle of the CART3. Results show that inclusion of the LIDAR measurement into the control system leads to further rejection of the wind disturbance at low frequencies when compared to feedback alone. This in turn provides confidence that LIDAR technology could be used to obtain load reductions with wind turbine controls.

@InProceedings{Scholbrock2013,
Title = {Field Testing {LIDAR} based Feed-forward Controls on the {NREL} Controls Advanced Research Turbine},
Author = {Scholbrock, Andrew and Fleming, Paul and Fingersh, Lee and Wright, Alan and Schlipf, David and Haizmann, Florian and Belen, Fred},
Booktitle = {Proceedings of the 51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition},
Year = {2013},
Abstract = {Researchers at the National Renewable Energy Laboratory (NREL) and the University of Stuttgart are designing, implementing, and testing advanced feedback and feed-forward controls for multimegawatt wind turbines that will help reduce the cost of wind energy. Past wind turbine controllers have depended on turbine feedback measurements to determine the controller pitch commands. In this setup, wind speed disturbances can only be corrected after their effects have been detected in the turbine’s loads and dynamic response, which causes a delayed control response due to turbine and pitch actuator dynamics. LIght Detection And Ranging (LIDAR) systems can provide information regarding the approaching wind field to the controller in advance, thereby increasing the controller’s available reaction time and allowing pitch actuation to occur in advance to mitigate wind disturbance effects. Feed-forward control algorithms that use these “look ahead” wind speed measurements can improve load mitigation and controller performance compared to feedback only controllers. This paper describes the development and field testing of a feed-forward collective pitch control algorithm to show its effects on speed regulation in above-rated wind speeds. The controller is implemented and field tested on one of the Controls Advanced Research Turbines (CARTs) at NREL. The wind speed measurements to the feed-forward controller are provided by BlueScout Technologies’ Optical Control System (OCS) LIDAR mounted on the nacelle of the CART3. Results show that inclusion of the LIDAR measurement into the control system leads to further rejection of the wind disturbance at low frequencies when compared to feedback alone. This in turn provides confidence that LIDAR technology could be used to obtain load reductions with wind turbine controls.},
Doi = {10.2514/6.2013-818},
Url = {http://dx.doi.org/10.2514/6.2013-818}
}

• E. Simley and L. Y. Pao, “Reducing lidar wind speed measurement error with optimal filtering,” in Proceedings of the american control conference, Washington, DC, USA, 2013. doi:10.1109/ACC.2013.6579906
Recent research has shown the potential for reduction in wind turbine generator speed error and structural loads with the introduction of feedforward control using preview LIDAR measurements. Several sources of error exist in the estimation of the wind speeds that will interact with the turbine rotor, including LIDAR distortion and coherence loss due to wind evolution. If a feedforward controller is designed assuming perfect wind speed measurements, however, the error in the disturbance estimate may cause feedforward control to increase output errors. Here we derive the minimum mean square error feedforward controller for imperfect measurements using statistical descriptions of the wind. We show that the resulting controller is the ideal feedforward controller, assuming perfect measurements, in series with a Wiener prefilter to reduce the mean square error of the disturbance estimate. We derive the optimal filter in the frequency domain assuming infinite preview as well as the optimal filter in the time domain with preview time constraints. Examples illustrating the error reduction with optimal prefiltering are provided for simulated control and measurement scenarios.

@InProceedings{Simley2013c,
Title = {Reducing LIDAR wind speed measurement error with optimal filtering},
Author = {Eric Simley and Lucy Y Pao},
Booktitle = {Proceedings of the American Control Conference},
Year = {2013},
Abstract = {Recent research has shown the potential for reduction in wind turbine generator speed error and structural loads with the introduction of feedforward control using preview LIDAR measurements. Several sources of error exist in the estimation of the wind speeds that will interact with the turbine rotor, including LIDAR distortion and coherence loss due to wind evolution. If a feedforward controller is designed assuming perfect wind speed measurements, however, the error in the disturbance estimate may cause feedforward control to increase output errors. Here we derive the minimum mean square error feedforward controller for imperfect measurements using statistical descriptions of the wind. We show that the resulting controller is the ideal feedforward controller, assuming perfect measurements, in series with a Wiener prefilter to reduce the mean square error of the disturbance estimate. We derive the optimal filter in the frequency domain assuming infinite preview as well as the optimal filter in the time domain with preview time constraints. Examples illustrating the error reduction with optimal prefiltering are provided for simulated control and measurement scenarios.},
Doi = {10.1109/ACC.2013.6579906},
Url = {http://dx.doi.org/10.1109/ACC.2013.6579906}
}

• E. Simley and L. Y. Pao, “Correlation between rotating LIDAR measurements and blade effective wind speed,” in Proceedings of the 51st aiaa aerospace sciences meeting including the new horizons forum and aerospace exposition, Dallas, USA, 2013. doi:10.2514/6.2013-749

@InProceedings{Simley2013a,
Title = {Correlation between Rotating {LIDAR} Measurements and Blade Effective Wind Speed},
Author = {Eric Simley and Lucy Y Pao},
Booktitle = {Proceedings of the 51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition},
Year = {2013},
until higher frequencies.},
Doi = {10.2514/6.2013-749},
Url = {http://dx.doi.org/10.2514/6.2013-749}
}

• C. Slinger, M. Leak, M. Pitter, and M. Harris, “Relative power curve measurements using turbine mounted, continuous-wave lidar,” in Proceedings of the european wind energy association annual event, Vienna, Austria, 2013.
Power curves are an important way of measuring turbine performance. IEC standard 61400-12-1 describes procedures to measure absolute power curves using ground-based metmasts. However, in many situations, it is more convenient and efficient to use turbine-mounted lidars to measure power curves. This arrangement ensures measurement of wind incident on the rotor irrespective of the wind direction, avoids theconsiderable difficulties associated with offshore metmast deployment, allows rapid measurement at multiple upwind ranges from the turbineand permits straightforward measurement of both hub height and rotor equivalent wind speeds. A circular scan lidar also permits a sampling of the wind field around the full rotor disk, and wake visualisation is also possible. Power curves derived from these measurements can allow turbine performance to be measured and compared pre-and post-intervention/adjustment/maintenance. The lidar measurements also allow calibration of other turbine instrumentation and permitting accurate measurement of yaw alignment and the investigation of the impact ofatmospheric effects such as vertical wind shear and turbulence. This paper presents the results of measurements from a nacelle-mounted, circular scanning continuous-wave ZephIR lidar, operating on a 2 MW onshore, horizontal axis turbine in Jutland, from January to April 2012. Lidar measurements at ranges from 10 m, 30 m, 50 m, 100 m and 180 m were taken (corresponding to ranges from 0.14 D to 2.5 D). Hub height wind speeds, wind yaw direction and vertical wind shear time series were obtained from these measurements. The measurements helped identify a consistent 14 to 16 degree yaw misalignment of the turbine. The pre and post yaw wind vane sensor calibration power curves are compared in the paper. The influence on power curves of lidar-measured high and low vertical wind shear, high and low atmospheric turbulence are also shown. By examining the wind speed at various ranges upwind of the turbine, the effects of turbine blade induction were also observed and measured. Turbulence and wind shear are found to have significant effects on the power curves. The measurements confirm that turbine-mounted, circular scan, continuous wave lidar can be a useful tool for turbine characterisation and instrumentation calibration. Measurement of turbine power curves, particularly offshore, is an important application area for turbine-mounted lidars.

@InProceedings{Slinger2013,
Title = {Relative power curve measurements using turbine mounted, continuous-wave lidar},
Author = {Slinger, C. and Leak, M. and Pitter, M. and Harris, M.},
Booktitle = {Proceedings of the European Wind Energy Association annual event},
Year = {2013},
Abstract = {Power curves are an important way of measuring turbine performance. IEC standard 61400-12-1 describes procedures to measure absolute power curves using ground-based metmasts. However, in many situations, it is more convenient and efficient to use turbine-mounted lidars to
measure power curves. This arrangement ensures measurement of wind incident on the rotor irrespective of the wind direction, avoids theconsiderable difficulties associated with offshore metmast deployment, allows rapid measurement at multiple upwind ranges from the turbineand permits straightforward measurement of both hub height and rotor equivalent wind speeds. A circular scan lidar also permits a sampling of the wind field around the full rotor disk, and wake visualisation is also possible. Power curves derived from these measurements can allow turbine performance to be measured and compared pre-and post-intervention/adjustment/maintenance. The lidar measurements also allow calibration of other turbine instrumentation and permitting accurate measurement of yaw alignment and the investigation of the impact ofatmospheric effects such as vertical wind shear and turbulence.
This paper presents the results of measurements from a nacelle-mounted, circular scanning continuous-wave ZephIR lidar, operating on a 2 MW onshore, horizontal axis turbine in Jutland, from January to April 2012. Lidar measurements at ranges from 10 m, 30 m, 50 m, 100 m and 180 m were taken (corresponding to ranges from 0.14 D to 2.5 D). Hub height wind speeds, wind yaw direction and vertical wind shear time series were obtained from these measurements. The measurements helped identify a consistent 14 to 16 degree yaw misalignment of the turbine. The pre and post yaw wind vane sensor calibration power curves are compared in the paper. The influence on power curves of lidar-measured high and low vertical wind shear, high and low atmospheric turbulence are also shown. By examining the wind speed at various ranges upwind of the turbine, the effects of turbine blade induction were also observed and measured. Turbulence and wind shear are found to have significant effects on the power curves.
The measurements confirm that turbine-mounted, circular scan, continuous wave lidar can be a useful tool for turbine characterisation and instrumentation calibration. Measurement of turbine power curves, particularly offshore, is an important application area for turbine-mounted lidars.},
}

• I. N. Smalikho, V. A. Banakh, Y. L. Pichugina, W. A. Brewer, R. M. Banta, J. K. Lundquist, and N. D. Kelley, “Lidar investigation of atmosphere effect on a wind turbine wake,” J. atmos. oceanic technol., vol. 30, iss. 11, pp. 2554-2570, 2013. doi:10.1175/JTECH-D-12-00108.1
An experimental study of the spatial wind structure in the vicinity of a wind turbine by a NOAA coherent Doppler lidar has been conducted. It was found that a working wind turbine generates a wake with the maximum velocity deficit varying from 27% to 74% and with the longitudinal dimension varying from 120 up to 1180 m, depending on the wind strength and atmospheric turbulence. It is shown that, at high wind speeds, the twofold increase of the turbulent energy dissipation rate (from 0.0066 to 0.013 m2 s−3) leads, on average, to halving of the longitudinal dimension of the wind turbine wake (from 680 to 340 m).

@Article{Smalikho2013,
Title = {Lidar Investigation of Atmosphere Effect on a Wind Turbine Wake},
Author = {Smalikho, I. N. and Banakh, V. A. and Pichugina, Y. L. and Brewer, W. A. and Banta, R. M. and Lundquist, J. K. and Kelley, N. D.},
Journal = {J. Atmos. Oceanic Technol.},
Year = {2013},
Number = {11},
Pages = {2554--2570},
Volume = {30},
Abstract = {An experimental study of the spatial wind structure in the vicinity of a wind turbine by a NOAA coherent Doppler lidar has been conducted. It was found that a working wind turbine generates a wake with the maximum velocity deficit varying from 27% to 74% and with the longitudinal dimension varying from 120 up to 1180 m, depending on the wind strength and atmospheric turbulence. It is shown that, at high wind speeds, the twofold increase of the turbulent energy dissipation rate (from 0.0066 to 0.013 m2 s−3) leads, on average, to halving of the longitudinal dimension of the wind turbine wake (from 680 to 340 m).},
Doi = {10.1175/JTECH-D-12-00108.1},
Url = {http://dx.doi.org/10.1175/JTECH-D-12-00108.1}
}

• N. Wang, K. E. Johnson, A. D. Wright, and C. E. Carcangiu, “LIDAR-assisted preview controllers design for a mw-scale commercial wind turbine model,” in Proceedings of the conference on decision and control, Florence, Italy, 2013. doi:10.1109/CDC.2013.6760123
Existing commercial wind turbine control algorithms are typically feedback only. Nacelle-based commercial light detection and ranging (LIDAR) systems, which can detect preview wind information in front of the turbine to be used in feedforward controller design, can improve wind turbine control performance compared to a baseline standard proportional-integral (PI) feedback controller. Combined feedforward and feedback collective pitch control strategies are investigated in this research for both mitigating tower fore-aft fatigue load above rated wind speed and enhancing power capture below rated wind speed. When the wind speed is above rated, we consider a collective pitch LQ-based preview control scheme that augments the existing feedback controller and uses a Kalman filter in the control loop as the observer. When the wind speed is below rated, we combine a tower foreaft feedback damping pitch controller with a feedforward controller designed through the method of Lagrange multipliers optimization. Control effectiveness verifications are conducted through FAST simulations with multiple turbulent wind cases.

@InProceedings{Wang2013a,
Title = {{LIDAR}-assisted preview controllers design for a MW-scale commercial wind turbine model},
Author = {Wang, Na and Johnson, Kathryn E. and Wright, Alan D. and Carcangiu, Carlo E.},
Booktitle = {Proceedings of the Conference on Decision and Control},
Year = {2013},
Abstract = {Existing commercial wind turbine control algorithms are typically feedback only. Nacelle-based commercial light detection and ranging (LIDAR) systems, which can detect preview wind information in front of the turbine to be used in feedforward controller design, can improve wind turbine control performance compared to a baseline standard proportional-integral (PI) feedback controller. Combined feedforward and feedback collective pitch control strategies are investigated in this research for both mitigating tower fore-aft fatigue load above rated wind speed and enhancing power capture below rated wind speed. When the wind speed is above rated, we consider a collective pitch LQ-based preview control scheme that augments the existing feedback controller and uses a Kalman filter in the control loop as the observer. When the wind speed is below rated, we combine a tower foreaft feedback damping pitch controller with a feedforward controller designed through the method of Lagrange multipliers optimization. Control effectiveness verifications are conducted through FAST simulations with multiple turbulent wind cases.},
Doi = {10.1109/CDC.2013.6760123},
Url = {http://dx.doi.org/10.1109/CDC.2013.6760123}
}

• N. Wang, K. E. Johnson, and A. D. Wright, “Comparison of strategies for enhancing energy capture and reducing loads using lidar and feedforward control,” Control systems technology, ieee transactions on, vol. 21, iss. 4, pp. 1129-1142, 2013. doi:10.1109/TCST.2013.2258670
In this paper, we investigate strategies to enhance turbine energy capture and mitigate fatigue loads using pulsed light detection and ranging (LIDAR) system-enabled torque control strategies. To enhance energy capture when a turbine is operating below rated wind speed, three advanced LIDAR-enabled torque controllers are proposed: the disturbance tracking control (DTC) augmented with LIDAR, the optimally tracking rotor (OTR) control augmented with LIDAR, and LIDAR-based preview control. The DTC with LIDAR and LIDAR-based preview control is combined with a linear quadratic regulator in the feedback path, while OTR is a strategy adapted from a quadratic kΩ2 torque feedback control. These control strategies are simulated in turbulent wind files and their performance is compared against the baseline kΩ2 control scheme. We also consider the effects of different LIDAR update rates and range gates. It is shown that LIDAR-enabled controllers have only a small effect on energy capture at the cost of increased control action and low-speed shaft torque load. However, when considering a combination of fatigue load mitigation, power capture enhancement, and control authority requirements, the LIDAR-enabled preview controller outperforms the baseline kΩ2 controller.

@Article{Wang2013b,
Title = {Comparison of Strategies for Enhancing Energy Capture and Reducing Loads Using LIDAR and Feedforward Control},
Author = {Wang, Na and Johnson, Kathryn E. and Wright, Alan D.},
Journal = {Control Systems Technology, IEEE Transactions on},
Year = {2013},
Number = {4},
Pages = {1129-1142},
Volume = {21},
Abstract = {In this paper, we investigate strategies to enhance turbine energy capture and mitigate fatigue loads using pulsed light detection and ranging (LIDAR) system-enabled torque control strategies. To enhance energy capture when a turbine is operating below rated wind speed, three advanced LIDAR-enabled torque controllers are proposed: the disturbance tracking control (DTC) augmented with LIDAR, the optimally tracking rotor (OTR) control augmented with LIDAR, and LIDAR-based preview control. The DTC with LIDAR and LIDAR-based preview control is combined with a linear quadratic regulator in the feedback path, while OTR is a strategy adapted from a quadratic kΩ2 torque feedback control. These control strategies are simulated in turbulent wind files and their performance is compared against the baseline kΩ2 control scheme. We also consider the effects of different LIDAR update rates and range gates. It is shown that LIDAR-enabled controllers have only a small effect on energy capture at the cost of increased control action and low-speed shaft torque load. However, when considering a combination of fatigue load mitigation, power capture enhancement, and control authority requirements, the LIDAR-enabled preview controller outperforms the baseline kΩ2 controller.},
Doi = {10.1109/TCST.2013.2258670},
Url = {http://dx.doi.org/10.1109/TCST.2013.2258670}
}

### 2012

• F. Dunne, D. Schlipf, L. Y. Pao, A. D. Wright, B. Jonkman, N. Kelley, and E. Simley, “Comparison of two independent lidar-based pitch control designs,” in Proceedings of the 50th aiaa aerospace sciences meeting including the new horizons forum and aerospace exposition, 2012. doi:10.2514/6.2012-1151
Two different lidar-based feedforward controllers have previously been designed for the NREL 5 MW wind turbine model under separate studies. One uses a finite-impulse-response design, with 5 seconds of preview, and three rotating lidar measurements. The other uses a static-gain design, with the preview time defined by the pitch actuator dynamics, a simulation of a real nacelle-based scanning lidar system, and a lowpass filter defined by the lidar configuration. These controllers are now directly compared under the same lidar configuration, in terms of fatigue load reduction, rotor speed regulation, and power capture. The various difierences in design choices are discussed and compared. We also compare frequency plots of individual pitch feedforward and collective pitch feedforward load reductions, and we see that individual pitch feedforward is effective mainly at the once-per-revolution and twice-per-revolution frequencies. We also explain how to determine the required preview time by breaking it down into separate parts, and we then compare it to the expected preview time available.

@InProceedings{Dunne2012,
Title = {Comparison of Two Independent Lidar-Based Pitch Control Designs},
Author = {Dunne, Fiona and Schlipf, David and Pao, Lucy Y and Wright, A D and Jonkman, Bonnie and Kelley, Neil and Simley, Eric},
Booktitle = {Proceedings of the 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition},
Year = {2012},
Abstract = {Two different lidar-based feedforward controllers have previously been designed for the NREL 5 MW wind turbine model under separate studies. One uses a finite-impulse-response design, with 5 seconds of preview, and three rotating lidar measurements. The other uses a static-gain design, with the preview time defined by the pitch actuator dynamics, a simulation of a real nacelle-based scanning lidar system, and a lowpass filter defined by the lidar configuration. These controllers are now directly compared under the same lidar configuration, in terms of fatigue load reduction, rotor speed regulation, and power capture. The various difierences in design choices are discussed and compared. We also compare frequency plots of individual pitch feedforward and collective pitch feedforward load reductions, and we see that individual pitch feedforward is effective mainly at the once-per-revolution and twice-per-revolution frequencies. We also explain how to determine the required preview time by breaking it down into separate parts, and we then compare it to the expected preview time available.},
Doi = {10.2514/6.2012-1151},
Url = {http://dx.doi.org/10.2514/6.2012-1151}
}

• B. D. Hirth, J. L. Schroeder, S. W. Gunter, and J. G. Guynes, “Measuring a utility-scale turbine wake using the ttuka mobile research radars,” Journal of atmospheric and oceanic technology, vol. 29, iss. 6, pp. 765-771, 2012. doi:10.1175/JTECH-D-12-00039.1
Observations of the wake generated by a single utility-scale turbine and collected by the Texas Tech University Ka-band mobile research radars on 27 October 2011 are introduced. Remotely sensed turbine wake observations using lidar technology have proven effective; however, the presented radar capabilities provide a larger observational footprint and greater along-beam resolution than current scanning lidar systems. Plan-position indicator and range–height indicator scanning techniques are utilized to produce various wake analyses. Preliminary analyses confirm radial velocity and wind speed deficits immediately downwind of the turbine hub to be on the order of 50\%. This introduction lays the groundwork for more in-depth analyses of wake structure and evolution using the Texas Tech University Ka-band radar systems, including wake meandering and wake-to-wake interaction in large wind park deployments.

@Article{Hirth2012,
Title = {Measuring a Utility-Scale Turbine Wake Using the TTUKa Mobile Research Radars},
Author = {Brian D. Hirth and John L. Schroeder and W. Scott Gunter and Jerry G. Guynes},
Journal = {Journal of Atmospheric and Oceanic Technology},
Year = {2012},
Number = {6},
Pages = {765-771},
Volume = {29},
Abstract = {Observations of the wake generated by a single utility-scale turbine and collected by the Texas Tech University Ka-band mobile research radars on 27 October 2011 are introduced. Remotely sensed turbine wake observations using lidar technology have proven effective; however, the presented radar capabilities provide a larger observational footprint and greater along-beam resolution than current scanning lidar systems. Plan-position indicator and range–height indicator scanning techniques are utilized to produce various wake analyses. Preliminary analyses confirm radial velocity and wind speed deficits immediately downwind of the turbine hub to be on the order of 50\%. This introduction lays the groundwork for more in-depth analyses of wake structure and evolution using the Texas Tech University Ka-band radar systems, including wake meandering and wake-to-wake interaction in large wind park deployments.},
Doi = {10.1175/JTECH-D-12-00039.1},
Url = {http://dx.doi.org/10.1175/JTECH-D-12-00039.1}
}

• S. Kapp and M. Kühn, “A five-parameter wind field estimation method based on spherical upwind lidar measurements,” in Proceedings of the science of making torque from wind, Oldenburg, Germany, 2012. doi:10.1088/1742-6596/555/1/012112
Turbine mounted scanning lidar systems of focussed continuous-wave type are taken into consideration to sense approaching wind fields. The quality of wind information depends on the lidar technology itself but also substantially on the scanning technique and reconstruction algorithm. In this paper a five-parameter wind field model comprising mean wind speed, vertical and horizontal linear shear and homogeneous direction angles is introduced. A corresponding parameter estimation method is developed based on the assumption of upwind lidar measurements scanned over spherical segments. As a main advantage of this method all relevant parameters, in terms of wind turbine control, can be provided. Moreover, the ability to distinguish between shear and skew potentially increases the quality of the resulting feedforward pitch angles when compared to three-parameter methods. It is shown that minimal three measurements, each in turn from two independent directions are necessary for the application of the algorithm, whereas simpler measurements, each taken from only one direction, are not sufficient.

@InProceedings{Kapp2012,
Title = {A Five-Parameter Wind Field Estimation Method Based on Spherical Upwind Lidar Measurements},
Author = {Kapp, Stefan and Kühn, Martin},
Booktitle = {Proceedings of The Science of Making Torque from Wind},
Year = {2012},
Abstract = {Turbine mounted scanning lidar systems of focussed continuous-wave type are taken into consideration to sense approaching wind fields. The quality of wind information depends on the lidar technology itself but also substantially on the scanning technique and reconstruction algorithm. In this paper a five-parameter wind field model comprising mean wind speed, vertical and horizontal linear shear and homogeneous direction angles is introduced. A corresponding parameter estimation method is developed based on the assumption of upwind lidar measurements scanned over spherical segments. As a main advantage of this method all relevant parameters, in terms of wind turbine control, can be provided. Moreover, the ability to distinguish between shear and skew potentially increases the quality of the resulting feedforward pitch angles when compared to three-parameter methods. It is shown that minimal three measurements, each in turn from two independent directions are necessary for the application of the algorithm, whereas simpler measurements, each taken from only one direction, are not sufficient.},
Doi = {10.1088/1742-6596/555/1/012112},
Url = {http://dx.doi.org/10.1088/1742-6596/555/1/012112}
}

• D. Schlipf, A. Rettenmeier, F. Haizmann, M. Hofsäß, M. Courtney, and P. W. Cheng, “Model based wind vector field reconstruction from lidar data,” in Proceedings of the german wind energy conference DEWEK, Bremen, Germany, 2012. doi:10.18419/opus-8136
In recent years lidar technology found its way into wind energy for resource assessment and control. For both fields of application it is crucial to reconstruct the wind field from the limited information provided by a lidar system. For lidar assisted wind turbine control model based wind field reconstruction is used to obtain signals from wind characteristics such as wind speed, direction and shears in a high temporal resolution. This work shows how these methods can be used for lidar based wind resource assessment in complex situations, where high accuracy is important, but cannot be archived by conventional technique. The reconstruction is validated for ground based lidar systems with measurement data and for floating lidar systems with detailed simulations.

@InProceedings{Schlipf2012f,
Title = {Model Based Wind Vector Field Reconstruction from Lidar Data},
Author = {David Schlipf and Andreas Rettenmeier and Florian Haizmann and Martin Hofsäß and Mike Courtney and Po Wen Cheng},
Booktitle = {Proceedings of the German Wind Energy Conference {DEWEK}},
Year = {2012},
Abstract = {In recent years lidar technology found its way into wind energy for resource assessment and control. For both fields of application it is crucial to reconstruct the wind field from the limited information provided by a lidar system. For lidar assisted wind turbine control model based wind field reconstruction is used to obtain signals from wind characteristics such as wind speed, direction and shears in a high temporal resolution. This work shows how these methods can be used for lidar based wind resource assessment in complex situations, where high accuracy is important, but cannot be archived by conventional technique. The reconstruction is validated for ground based lidar systems with measurement data and for floating lidar systems with detailed simulations.},
Doi = {10.18419/opus-8136},
Url = {http://dx.doi.org/10.18419/opus-8136}
}

• D. Schlipf, L. Y. Pao, and P. W. Cheng, “Comparison of feedforward and model predictive control of wind turbines using LIDAR,” in Proceedings of the conference on decision and control, Maui, USA, 2012. doi:10.1109/CDC.2012.6426063
LIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller and a feedforward controller to a baseline controller. Realistic wind “measurements” are obtained using a detailed simulation of a LIDAR system. A full lifetime comparison shows the advantages of using the wind predictions to reduce wind turbine fatigue loads on the tower and blades as well as to limit the blade pitch rates. The results illustrate that the feedforward controller can be combined with a tower feedback controller to yield similar load reductions as the model predictive controller.

@InProceedings{Schlipf2012g,
Title = {Comparison of Feedforward and Model Predictive Control of Wind Turbines Using {LIDAR}},
Author = {David Schlipf and Lucy Y. Pao and Po Wen Cheng},
Booktitle = {Proceedings of the Conference on Decision and Control},
Year = {2012},
Abstract = {LIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller and a feedforward controller to a baseline controller. Realistic wind “measurements” are obtained using a detailed simulation of a LIDAR system. A full lifetime comparison shows the advantages of using the wind predictions to reduce wind turbine fatigue loads on the tower and blades as well as to limit the blade pitch rates. The results illustrate that the feedforward controller can be combined with a tower feedback controller to yield similar load reductions as the model predictive controller.},
Doi = {10.1109/CDC.2012.6426063},
Url = {http://dx.doi.org/10.1109/CDC.2012.6426063}
}

• E. Simley, L. Y. Pao, N. Kelley, B. Jonkman, and R. Frehlich, “LIDAR wind speed measurements of evolving wind fields,” in Proceedings of the 50th aiaa aerospace sciences meeting including the new horizons forum and aerospace exposition, Nashville, USA, 2012. doi:10.2514/6.2012-656
Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. Past studies have assumed Taylor’s frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylor’s hypothesis applied, the only source of wind speed measurement error is distortion caused by the LIDAR. This study introduces wind evolution, characterized by the longitudinal coherence of the wind, to LIDAR measurement simulations to create a more realistic measurement model. A simple model of wind evolution is applied to a frozen wind field used in previous studies to investigate the effects of varying the intensity of wind evolution. Simulation results show the combined effects of LIDAR errors and wind evolution for realistic turbine-mounted LIDAR measurement scenarios.

@InProceedings{Simley2012a,
Title = {{LIDAR} Wind Speed Measurements of Evolving Wind Fields},
Author = {Eric Simley and Lucy Y Pao and Kelley, Neil and Jonkman, Bonnie and Frehlich, Rod},
Booktitle = {Proceedings of the 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition},
Year = {2012},
Abstract = {Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. Past studies have assumed Taylor's frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylor's hypothesis applied, the only source of wind speed measurement error is distortion caused by the LIDAR. This study introduces wind evolution, characterized by the longitudinal coherence of the wind, to LIDAR measurement simulations to create a more realistic measurement model. A simple model of wind evolution is applied to a frozen wind field used in previous studies to investigate the effects of varying the intensity of wind evolution. Simulation results show the combined effects of LIDAR errors and wind evolution for realistic turbine-mounted LIDAR measurement scenarios.},
Doi = {10.2514/6.2012-656},
Url = {http://dx.doi.org/10.2514/6.2012-656}
}

• N. Wang, K. E. Johnson, and A. D. Wright, “Fx-rls-based feedforward control for lidar-enabled wind turbine load mitigation,” Control systems technology, ieee transactions on, vol. 20, iss. 5, pp. 1212-1222, 2012. doi:10.1109/TCST.2011.2163515
An adaptive feedforward controller based on a filtered-x recursive least square (FX-RLS) algorithm and a non-adaptive feedforward controller based on a zero-phase-error tracking control (ZPETC) technique have been designed to augment a collective pitch proportional-integral (PI) feedback controller for wind turbine rotor speed regulation and component load reduction when the wind turbine is operating above rated wind speed. The inputs to the adaptive feedforward controller include measurements of the rotor speed error and the incoming wind speed, where wind speed would be provided by a commercial light detection and ranging (LIDAR) system. Simulation results are based on comparison with a PI feedback only controller. Simulations show that augmenting the baseline PI feedback control with ZPETC feedforward control improves the blade loads but worsens the tower loads. The FX-RLS feedforward algorithm gives better performance than both the baseline PI feedback and the ZPETC feedforward in both tower (fore-aft and side-to-side) and blade (flapwise and edgewise) bending moment mitigation. Even with realistic 1 Hz LIDAR data update rate, the FX-RLS feedforward strategy can effectively mitigate the tower and blade bending moment while providing better rotor speed tracking and only a small energy drop.

@Article{Wang2012,
Title = {FX-RLS-Based Feedforward Control for LIDAR-Enabled Wind Turbine Load Mitigation},
Author = {Wang, Na and Johnson, Kathryn E. and Wright, Alan D.},
Journal = {Control Systems Technology, IEEE Transactions on},
Year = {2012},
Number = {5},
Pages = {1212-1222},
Volume = {20},
Abstract = {An adaptive feedforward controller based on a filtered-x recursive least square (FX-RLS) algorithm and a non-adaptive feedforward controller based on a zero-phase-error tracking control (ZPETC) technique have been designed to augment a collective pitch proportional-integral (PI) feedback controller for wind turbine rotor speed regulation and component load reduction when the wind turbine is operating above rated wind speed. The inputs to the adaptive feedforward controller include measurements of the rotor speed error and the incoming wind speed, where wind speed would be provided by a commercial light detection and ranging (LIDAR) system. Simulation results are based on comparison with a PI feedback only controller. Simulations show that augmenting the baseline PI feedback control with ZPETC feedforward control improves the blade loads but worsens the tower loads. The FX-RLS feedforward algorithm gives better performance than both the baseline PI feedback and the ZPETC feedforward in both tower (fore-aft and side-to-side) and blade (flapwise and edgewise) bending moment mitigation. Even with realistic 1 Hz LIDAR data update rate, the FX-RLS feedforward strategy can effectively mitigate the tower and blade bending moment while providing better rotor speed tracking and only a small energy drop.},
Doi = {10.1109/TCST.2011.2163515},
Url = {http://dx.doi.org/10.1109/TCST.2011.2163515}
}