Articles | Volume 4, issue 2
https://doi.org/10.5194/wes-4-273-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-4-273-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 May 2019
Research article |
| 20 May 2019
| 20 May 2019
Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Jennifer King
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Katherine Dykes
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Eric Simley
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Jason Roadman
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Andrew Scholbrock
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Patrick Murphy
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Dept. Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
Julie K. Lundquist
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Dept. Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
Patrick Moriarty
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Katherine Fleming
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Jeroen van Dam
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Christopher Bay
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Rafael Mudafort
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Hector Lopez
NextEra Energy Resources, 700 Universe Blvd, Juno Beach, FL 33408
Jason Skopek
NextEra Energy Resources, 700 Universe Blvd, Juno Beach, FL 33408
Michael Scott
NextEra Energy Resources, 700 Universe Blvd, Juno Beach, FL 33408
Brady Ryan
NextEra Energy Resources, 700 Universe Blvd, Juno Beach, FL 33408
Charles Guernsey
NextEra Energy Resources, 700 Universe Blvd, Juno Beach, FL 33408
Dan Brake
NextEra Energy Resources, 700 Universe Blvd, Juno Beach, FL 33408
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Cited
114 citations as recorded by crossref.
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
- Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model C. Bay et al. 10.5194/wes-8-401-2023
- Modelling and assessing the near-wake representation and turbulence behaviour of control-oriented wake models P. Hulsman et al. 10.1088/1742-6596/1618/2/022056
- Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data L. Andersson et al. 10.1088/1742-6596/1618/2/022043
- Experimental and numerical study of the wake deflections of scaled vertical axis wind turbine models M. Huang et al. 10.1088/1742-6596/2505/1/012019
- Control-oriented model for secondary effects of wake steering J. King et al. 10.5194/wes-6-701-2021
- Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al. 10.1017/jfm.2021.237
- Highlighting the impact of yaw control by parsing atmospheric conditions based on total variation N. Hamilton 10.1088/1742-6596/1452/1/012006
- Algorithms to harvest the wind D. Monroe 10.1145/3379497
- Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. 10.5194/wes-7-741-2022
- Blade planform design optimization to enhance turbine wake control J. Allen et al. 10.1002/we.2699
- Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant E. Simley et al. 10.1088/1742-6596/2265/3/032097
- A point vortex transportation model for yawed wind turbine wakes H. Zong & F. Porté-Agel 10.1017/jfm.2020.123
- A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
- How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. 10.5194/wes-5-1169-2020
- Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering F. Campagnolo et al. 10.3389/fenrg.2022.883889
- Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data L. Wang et al. 10.1016/j.energy.2024.130401
- Wake steering of multirotor wind turbines G. Speakman et al. 10.1002/we.2633
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- Field Validation of Wake Steering Control with Wind Direction Variability E. Simley et al. 10.1088/1742-6596/1452/1/012012
- Wake position tracking using dynamic wake meandering model and rotor loads L. Dong et al. 10.1063/5.0032917
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
- A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
- Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. 10.1063/5.0023746
- Numerical Validation of Wind Plant Control Strategies S. Gomez-Iradi et al. 10.1088/1742-6596/1618/2/022010
- Enabling control co-design of the next generation of wind power plants A. Stanley et al. 10.5194/wes-8-1341-2023
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Wind energy-harvesting technologies and recent research progresses in wind farm control models B. Desalegn et al. 10.3389/fenrg.2023.1124203
- Real-time optimization of wind farms using modifier adaptation and machine learning L. Andersson & L. Imsland 10.5194/wes-5-885-2020
- Experimental analysis of the effect of dynamic induction control on a wind turbine wake D. van der Hoek et al. 10.5194/wes-7-1305-2022
- Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al. 10.5194/wes-5-469-2020
- Experimental investigation and analytical modelling of active yaw control for wind farm power optimization H. Zong & F. Porté-Agel 10.1016/j.renene.2021.02.059
- Characterizing tilt effects on wind plants R. Scott et al. 10.1063/5.0009853
- Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. 10.5194/wes-5-1273-2020
- A yawed wake model to predict the velocity distribution of curled wake cross-section for wind turbines Q. Yang et al. 10.1016/j.oceaneng.2024.116911
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- On the Robustness of Active Wake Control to Wind Turbine Downtime S. Kanev 10.3390/en12163152
- Wake redirection for active power control: a realistic case study M. Kretschmer et al. 10.1088/1742-6596/1618/2/022059
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- How does the rotational direction of an upwind turbine affect its downwind neighbour? A. Englberger et al. 10.1088/1742-6596/2265/2/022048
- Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. 10.1088/1742-6596/2265/3/032074
- Real-time identification of clusters of turbines F. Bernardoni et al. 10.1088/1742-6596/1618/2/022032
- Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. 10.1088/1742-6596/2265/2/022019
- Lifetime fatigue response due to wake steering on a pair of utility-scale wind turbines S. Dana et al. 10.1088/1742-6596/2265/2/022106
- A CFD‐based analysis of dynamic induction techniques for wind farm control applications A. Croce et al. 10.1002/we.2801
- U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. 10.1029/2019GL082636
- On the load impact of dynamic wind farm wake mixing strategies J. Frederik & J. van Wingerden 10.1016/j.renene.2022.05.110
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
- Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. 10.5194/wes-5-945-2020
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden 10.1088/1742-6596/1618/2/022023
- Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al. 10.5194/wes-8-1071-2023
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. 10.5194/wes-8-691-2023
- Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. 10.5194/wes-6-1427-2021
- A time‐varying formulation of the curled wake model within the FAST.Farm framework E. Branlard et al. 10.1002/we.2785
- Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow P. Hulsman et al. 10.5194/wes-7-237-2022
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- Near-wake structure of full-scale vertical-axis wind turbines N. Wei et al. 10.1017/jfm.2020.578
- Assessment of yaw-control effects on wind turbine-wake interaction: A coupled unsteady vortex lattice method and curled wake model analysis W. Han et al. 10.1016/j.jweia.2023.105559
- Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy B. Doekemeijer et al. 10.5194/wes-6-159-2021
- A dynamic model of wind turbine yaw for active farm control G. Starke et al. 10.1002/we.2884
- Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models P. Brugger et al. 10.5194/wes-5-1253-2020
- A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha 10.1115/1.4054501
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Koopman Model Predictive Control for Wind Farm Yield Optimization with Combined Thrust and Yaw Control A. Dittmer et al. 10.1016/j.ifacol.2023.10.1037
- Wind farm power optimization through wake steering M. Howland et al. 10.1073/pnas.1903680116
- Decreasing wind speed extrapolation error via domain-specific feature extraction and selection D. Vassallo et al. 10.5194/wes-5-959-2020
- Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
- Experimental investigation of wind turbine wake and load dynamics during yaw maneuvers S. Macrí et al. 10.5194/wes-6-585-2021
- Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. 10.1038/s41560-022-01085-8
- Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon 10.5194/wes-9-495-2024
- The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake J. Rinker et al. 10.3390/en14217313
- The Jensen wind farm parameterization Y. Ma et al. 10.5194/wes-7-2407-2022
- Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow M. Mohammadi et al. 10.3390/en15239135
- A new method to characterize the curled wake shape under yaw misalignment B. Sengers et al. 10.1088/1742-6596/1618/6/062050
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- Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al. 10.1017/jfm.2021.737
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- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
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- Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. 10.1088/1742-6596/1452/1/012023
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114 citations as recorded by crossref.
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
- Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model C. Bay et al. 10.5194/wes-8-401-2023
- Modelling and assessing the near-wake representation and turbulence behaviour of control-oriented wake models P. Hulsman et al. 10.1088/1742-6596/1618/2/022056
- Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data L. Andersson et al. 10.1088/1742-6596/1618/2/022043
- Experimental and numerical study of the wake deflections of scaled vertical axis wind turbine models M. Huang et al. 10.1088/1742-6596/2505/1/012019
- Control-oriented model for secondary effects of wake steering J. King et al. 10.5194/wes-6-701-2021
- Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al. 10.1017/jfm.2021.237
- Highlighting the impact of yaw control by parsing atmospheric conditions based on total variation N. Hamilton 10.1088/1742-6596/1452/1/012006
- Algorithms to harvest the wind D. Monroe 10.1145/3379497
- Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. 10.5194/wes-7-741-2022
- Blade planform design optimization to enhance turbine wake control J. Allen et al. 10.1002/we.2699
- Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant E. Simley et al. 10.1088/1742-6596/2265/3/032097
- A point vortex transportation model for yawed wind turbine wakes H. Zong & F. Porté-Agel 10.1017/jfm.2020.123
- A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
- How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. 10.5194/wes-5-1169-2020
- Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering F. Campagnolo et al. 10.3389/fenrg.2022.883889
- Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data L. Wang et al. 10.1016/j.energy.2024.130401
- Wake steering of multirotor wind turbines G. Speakman et al. 10.1002/we.2633
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- Field Validation of Wake Steering Control with Wind Direction Variability E. Simley et al. 10.1088/1742-6596/1452/1/012012
- Wake position tracking using dynamic wake meandering model and rotor loads L. Dong et al. 10.1063/5.0032917
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
- A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
- Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. 10.1063/5.0023746
- Numerical Validation of Wind Plant Control Strategies S. Gomez-Iradi et al. 10.1088/1742-6596/1618/2/022010
- Enabling control co-design of the next generation of wind power plants A. Stanley et al. 10.5194/wes-8-1341-2023
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Wind energy-harvesting technologies and recent research progresses in wind farm control models B. Desalegn et al. 10.3389/fenrg.2023.1124203
- Real-time optimization of wind farms using modifier adaptation and machine learning L. Andersson & L. Imsland 10.5194/wes-5-885-2020
- Experimental analysis of the effect of dynamic induction control on a wind turbine wake D. van der Hoek et al. 10.5194/wes-7-1305-2022
- Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al. 10.5194/wes-5-469-2020
- Experimental investigation and analytical modelling of active yaw control for wind farm power optimization H. Zong & F. Porté-Agel 10.1016/j.renene.2021.02.059
- Characterizing tilt effects on wind plants R. Scott et al. 10.1063/5.0009853
- Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. 10.5194/wes-5-1273-2020
- A yawed wake model to predict the velocity distribution of curled wake cross-section for wind turbines Q. Yang et al. 10.1016/j.oceaneng.2024.116911
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- On the Robustness of Active Wake Control to Wind Turbine Downtime S. Kanev 10.3390/en12163152
- Wake redirection for active power control: a realistic case study M. Kretschmer et al. 10.1088/1742-6596/1618/2/022059
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- How does the rotational direction of an upwind turbine affect its downwind neighbour? A. Englberger et al. 10.1088/1742-6596/2265/2/022048
- Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. 10.1088/1742-6596/2265/3/032074
- Real-time identification of clusters of turbines F. Bernardoni et al. 10.1088/1742-6596/1618/2/022032
- Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. 10.1088/1742-6596/2265/2/022019
- Lifetime fatigue response due to wake steering on a pair of utility-scale wind turbines S. Dana et al. 10.1088/1742-6596/2265/2/022106
- A CFD‐based analysis of dynamic induction techniques for wind farm control applications A. Croce et al. 10.1002/we.2801
- U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. 10.1029/2019GL082636
- On the load impact of dynamic wind farm wake mixing strategies J. Frederik & J. van Wingerden 10.1016/j.renene.2022.05.110
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
- Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. 10.5194/wes-5-945-2020
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden 10.1088/1742-6596/1618/2/022023
- Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al. 10.5194/wes-8-1071-2023
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Latest update: 18 Apr 2024
Short summary
Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes...
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