Articles | Volume 5, issue 2
https://doi.org/10.5194/wes-5-451-2020
© 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-5-451-2020
© 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 Apr 2020
Research article |
| 08 Apr 2020
| 08 Apr 2020
Design and analysis of a wake steering controller with wind direction variability
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Paul Fleming
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
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58 citations as recorded by crossref.
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- 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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- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha 10.1115/1.4054501
- Analysis of horizontal wind direction variability considering different influencing factors Z. Shu et al. 10.1016/j.jweia.2024.105819
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- A Tutorial on the Control of Floating Offshore Wind Turbines: Stability Challenges and Opportunities for Power Capture D. Stockhouse et al. 10.1109/MCS.2024.3433208
- Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol 10.1063/5.0107244
- 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
- The Jensen wind farm parameterization Y. Ma et al. 10.5194/wes-7-2407-2022
- Study on the yaw-based wake steering control considering dynamic flow characteristics for wind farm power improvement X. Yu et al. 10.1088/1742-6596/2505/1/012010
- 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
- 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
- Deep reinforcement learning-driven wind farm flow control considering dynamic wind H. Wang et al. 10.1016/j.enconman.2025.119888
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering D. Onnen et al. 10.1088/1742-6596/2265/2/022024
- Data-driven yaw misalignment correction for utility-scale wind turbines L. Gao & J. Hong 10.1063/5.0056671
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- An open-source framework for the development, deployment and testing of wind farm control strategies C. Sucameli et al. 10.1088/1742-6596/2767/9/092043
- Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings M. Sinner et al. 10.1063/5.0039899
- Numerical investigation of rotor asymmetry to promote wake recovery A. Abraham et al. 10.1088/1742-6596/2505/1/012032
- Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. 10.5194/wes-5-1273-2020
- Rotating mesh framework and dynamic wind direction variability effects on building ventilation with large eddy simulation H. Zhu et al. 10.1063/5.0267647
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- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
- 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
- Reinforcement learning for wind-farm flow control: Current state and future actions M. Abkar et al. 10.1016/j.taml.2023.100475
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- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- Wind-farm power prediction using a turbulence-optimized Gaussian wake model N. Zehtabiyan-Rezaie et al. 10.1016/j.weer.2024.100007
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- Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression D. Hoek et al. 10.1088/1742-6596/1618/2/022024
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- Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon 10.5194/wes-9-495-2024
- Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
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- The value of wake steering wind farm flow control in US energy markets E. Simley et al. 10.5194/wes-9-219-2024
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- Error analysis of low-fidelity models for wake steering based on field measurements S. Letizia et al. 10.1088/1742-6596/2767/4/042029
- Modeling dynamic wind direction changes in large eddy simulations of wind farms A. Stieren et al. 10.1016/j.renene.2021.02.018
- Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation M. Howland & J. Dabiri 10.3390/en14010052
- Experimental analysis of co-rotating and counter-rotating tandem horizontal-axis wind turbine performance and wake dynamics P. Bayron et al. 10.1016/j.jweia.2024.105840
- Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models P. Brugger et al. 10.5194/wes-5-1253-2020
- Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al. 10.1088/1742-6596/2767/3/032049
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Control Co-Design of Wind Turbines L. Pao et al. 10.1146/annurev-control-061423-101708
- A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies M. Becker et al. 10.5194/wes-10-1055-2025
4 citations as recorded by crossref.
- 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
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- 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
Latest update: 09 Jul 2025
Short summary
Wind farm wake losses occur when turbines operate in the wakes of upstream turbines. However, wake steering control can be used to deflect wakes away from downstream turbines. A method for including wind direction variability in wake steering simulations is presented here. Controller performance is shown to improve when wind direction variability is accounted for. Furthermore, the importance of wind direction variability is shown for different turbine spacings and atmospheric conditions.
Wind farm wake losses occur when turbines operate in the wakes of upstream turbines. However,...
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