Articles | Volume 3, issue 2
https://doi.org/10.5194/wes-3-869-2018
© Author(s) 2018. 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-3-869-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Nov 2018
Research article |
| 12 Nov 2018
| 12 Nov 2018
Robust active wake control in consideration of wind direction variability and uncertainty
ForWind – Center for Wind Energy Research, Institute of Physics,
University of Oldenburg, 26129 Oldenburg, Germany
Bart Doekemeijer
Delft Center for Systems and Control, Delft University of
Technology, 2628 CD Delft, the Netherlands
Janna Kristina Seifert
ForWind – Center for Wind Energy Research, Institute of Physics,
University of Oldenburg, 26129 Oldenburg, Germany
Jan-Willem van Wingerden
Delft Center for Systems and Control, Delft University of
Technology, 2628 CD Delft, the Netherlands
Martin Kühn
ForWind – Center for Wind Energy Research, Institute of Physics,
University of Oldenburg, 26129 Oldenburg, Germany
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Cited
46 citations as recorded by crossref.
- 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
- Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur 10.3390/app12084013
- An analytical modeling study on yaw-based wake redirection control for large-scale offshore wind farm annual energy power improvement J. Tan et al. 10.1063/5.0207111
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- Deep reinforcement learning-driven wind farm flow control considering dynamic wind H. Wang et al. 10.1016/j.enconman.2025.119888
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. 10.5194/wes-5-1273-2020
- 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
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- 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 Low-Computational Physics-Guided Deep Learning Model for Wind Farm Flow Control under Time-varying Wind Conditions S. He et al. 10.1016/j.energy.2025.137048
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm S. Shid-Moosavi et al. 10.1016/j.renene.2024.122126
- Quantification of parameter uncertainty in wind farm wake modeling J. Zhang & X. Zhao 10.1016/j.energy.2020.117065
- Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al. 10.5194/wes-8-1071-2023
- Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling K. Chen et al. 10.1109/TII.2022.3157302
- 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
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
- Smart cooperative control scheme for large-scale wind farms based on a double-layer machine learning framework S. Yang et al. 10.1016/j.enconman.2023.116949
- Wake steering experiments in onshore and offshore wind farms J. Pena Martinez & J. Coussy 10.1088/1742-6596/2767/9/092090
- Wind Farm Yield and Lifetime Optimization by Smart Steering of Wakes J. Schmidt et al. 10.1088/1742-6596/1934/1/012020
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- Lifetime extension of waked wind farms using active power control M. Vali et al. 10.1088/1742-6596/1256/1/012029
- Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms K. Klemmer et al. 10.1063/5.0166830
- Optimization of wind farm power output using wake redirection control R. Balakrishnan et al. 10.1016/j.renene.2024.121357
- 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
- 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
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- Dynamic wake conditions tailored by an active grid in the wind tunnel D. Onnen et al. 10.1088/1742-6596/2767/4/042038
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
- Axial induction controller field test at Sedini wind farm E. Bossanyi & R. Ruisi 10.5194/wes-6-389-2021
- Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al. 10.1088/1742-6596/2767/3/032049
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Wind vane correction during yaw misalignment for horizontal-axis wind turbines A. Rott et al. 10.5194/wes-8-1755-2023
- Analysis of horizontal wind direction variability considering different influencing factors Z. Shu et al. 10.1016/j.jweia.2024.105819
- LSTM-NN Yaw Control of Wind Turbines Based on Upstream Wind Information W. Chen et al. 10.3390/en13061482
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- 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
- A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control R. He et al. 10.1016/j.apenergy.2022.120013
- Field Validation of Wake Steering Control with Wind Direction Variability E. Simley et al. 10.1088/1742-6596/1452/1/012012
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
46 citations as recorded by crossref.
- 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
- Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur 10.3390/app12084013
- An analytical modeling study on yaw-based wake redirection control for large-scale offshore wind farm annual energy power improvement J. Tan et al. 10.1063/5.0207111
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- Deep reinforcement learning-driven wind farm flow control considering dynamic wind H. Wang et al. 10.1016/j.enconman.2025.119888
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. 10.5194/wes-5-1273-2020
- 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
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- 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 Low-Computational Physics-Guided Deep Learning Model for Wind Farm Flow Control under Time-varying Wind Conditions S. He et al. 10.1016/j.energy.2025.137048
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm S. Shid-Moosavi et al. 10.1016/j.renene.2024.122126
- Quantification of parameter uncertainty in wind farm wake modeling J. Zhang & X. Zhao 10.1016/j.energy.2020.117065
- Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al. 10.5194/wes-8-1071-2023
- Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling K. Chen et al. 10.1109/TII.2022.3157302
- 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
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
- Smart cooperative control scheme for large-scale wind farms based on a double-layer machine learning framework S. Yang et al. 10.1016/j.enconman.2023.116949
- Wake steering experiments in onshore and offshore wind farms J. Pena Martinez & J. Coussy 10.1088/1742-6596/2767/9/092090
- Wind Farm Yield and Lifetime Optimization by Smart Steering of Wakes J. Schmidt et al. 10.1088/1742-6596/1934/1/012020
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- Lifetime extension of waked wind farms using active power control M. Vali et al. 10.1088/1742-6596/1256/1/012029
- Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms K. Klemmer et al. 10.1063/5.0166830
- Optimization of wind farm power output using wake redirection control R. Balakrishnan et al. 10.1016/j.renene.2024.121357
- 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
- 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
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- Dynamic wake conditions tailored by an active grid in the wind tunnel D. Onnen et al. 10.1088/1742-6596/2767/4/042038
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
- Axial induction controller field test at Sedini wind farm E. Bossanyi & R. Ruisi 10.5194/wes-6-389-2021
- Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al. 10.1088/1742-6596/2767/3/032049
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Wind vane correction during yaw misalignment for horizontal-axis wind turbines A. Rott et al. 10.5194/wes-8-1755-2023
- Analysis of horizontal wind direction variability considering different influencing factors Z. Shu et al. 10.1016/j.jweia.2024.105819
- LSTM-NN Yaw Control of Wind Turbines Based on Upstream Wind Information W. Chen et al. 10.3390/en13061482
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- 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
- A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control R. He et al. 10.1016/j.apenergy.2022.120013
- Field Validation of Wake Steering Control with Wind Direction Variability E. Simley et al. 10.1088/1742-6596/1452/1/012012
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
Latest update: 21 Jun 2025
Short summary
Active wake deflection (AWD) aims to increase the power output of a wind farm by misaligning the yaw of upstream turbines. We analysed the effect of dynamic wind direction changes on AWD. The results show that AWD is very sensitive towards these dynamics. Therefore, we present a robust active wake control, which considers uncertainties and wind direction changes, increasing the overall power output of a wind farm. A side effect is a significant reduction of the yaw actuation of the turbines.
Active wake deflection (AWD) aims to increase the power output of a wind farm by misaligning the...
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