Articles | Volume 5, issue 4
https://doi.org/10.5194/wes-5-1253-2020
© Author(s) 2020. 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-5-1253-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Oct 2020
Research article |
| 08 Oct 2020
| 08 Oct 2020
Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models
Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fedérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
Mithu Debnath
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
Andrew Scholbrock
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
Paul Fleming
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
Patrick Moriarty
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
Eric Simley
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
David Jager
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
Jason Roadman
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
Mark Murphy
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
Haohua Zong
Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fedérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
Fernando Porté-Agel
Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fedérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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Cited
24 citations as recorded by crossref.
- Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al. 10.1017/jfm.2021.237
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number P. Brugger et al. 10.5194/wes-9-1363-2024
- Wake steering of multirotor wind turbines G. Speakman et al. 10.1002/we.2633
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- A wind tunnel investigation of yawed wind turbine wake impacts on downwind wind turbine performances and wind loads T. Uchida et al. 10.1177/0309524X221150219
- Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain R. Robey & J. Lundquist 10.5194/wes-9-1905-2024
- Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model J. Serrano González et al. 10.3390/en14040938
- An Effect of Wind Veer on Wind Turbine Performance U. Tumenbayar & K. Ko 10.14710/ijred.2023.47905
- Characteristics and modelling of wake for aligned multiple turbines based on numerical simulation R. Zhang et al. 10.1016/j.jweia.2022.105097
- Comparison of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes I. Rivera-Arreba et al. 10.1016/j.renene.2023.119807
- 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
- A data-driven layout optimization framework of large-scale wind farms based on machine learning K. Yang et al. 10.1016/j.renene.2023.119240
- Error analysis of low-fidelity models for wake steering based on field measurements S. Letizia et al. 10.1088/1742-6596/2767/4/042029
- A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements Z. Luo et al. 10.1016/j.energy.2024.130772
- A pressure-driven atmospheric boundary layer model satisfying Rossby and Reynolds number similarity M. van der Laan et al. 10.5194/wes-6-777-2021
- Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems T. Wetz & N. Wildmann 10.5194/wes-8-515-2023
- A novel 2D analytical model for predicting single and multiple‐interacting wakes of horizontal‐axis wind turbines P. Asad Ayoubi et al. 10.1002/ep.13980
- Measuring wake deflection from SCADA data during wake steering using machine learning N. Post et al. 10.1088/1742-6596/2767/4/042031
- Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach Z. Luo et al. 10.1016/j.renene.2024.121552
- Wind Farm Power Maximization through Wake Steering with a New Multiple Wake Model for Prediction of Turbulence Intensity G. Qian & T. Ishihara 10.1016/j.energy.2020.119680
23 citations as recorded by crossref.
- Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al. 10.1017/jfm.2021.237
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number P. Brugger et al. 10.5194/wes-9-1363-2024
- Wake steering of multirotor wind turbines G. Speakman et al. 10.1002/we.2633
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- A wind tunnel investigation of yawed wind turbine wake impacts on downwind wind turbine performances and wind loads T. Uchida et al. 10.1177/0309524X221150219
- Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain R. Robey & J. Lundquist 10.5194/wes-9-1905-2024
- Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model J. Serrano González et al. 10.3390/en14040938
- An Effect of Wind Veer on Wind Turbine Performance U. Tumenbayar & K. Ko 10.14710/ijred.2023.47905
- Characteristics and modelling of wake for aligned multiple turbines based on numerical simulation R. Zhang et al. 10.1016/j.jweia.2022.105097
- Comparison of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes I. Rivera-Arreba et al. 10.1016/j.renene.2023.119807
- 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
- A data-driven layout optimization framework of large-scale wind farms based on machine learning K. Yang et al. 10.1016/j.renene.2023.119240
- Error analysis of low-fidelity models for wake steering based on field measurements S. Letizia et al. 10.1088/1742-6596/2767/4/042029
- A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements Z. Luo et al. 10.1016/j.energy.2024.130772
- A pressure-driven atmospheric boundary layer model satisfying Rossby and Reynolds number similarity M. van der Laan et al. 10.5194/wes-6-777-2021
- Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems T. Wetz & N. Wildmann 10.5194/wes-8-515-2023
- A novel 2D analytical model for predicting single and multiple‐interacting wakes of horizontal‐axis wind turbines P. Asad Ayoubi et al. 10.1002/ep.13980
- Measuring wake deflection from SCADA data during wake steering using machine learning N. Post et al. 10.1088/1742-6596/2767/4/042031
- Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach Z. Luo et al. 10.1016/j.renene.2024.121552
Latest update: 21 Nov 2024
Short summary
A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
A wind turbine can actively influence its wake by turning the rotor out of the wind direction to...
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