Articles | Volume 5, issue 1
https://doi.org/10.5194/wes-5-309-2020
© 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-5-309-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Mar 2020
Research article |
| 05 Mar 2020
| 05 Mar 2020
Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations
Paul Hulsman
ForWind – University of Oldenburg, Research Group Wind Energy Systems, Küpkersweg 70, 26129 Oldenburg, Germany
Wind Energy Department, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Lyngby, Denmark
Tuhfe Göçmen
Wind Energy Department, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
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Cited
47 citations as recorded by crossref.
- Wind farm set point optimization with surrogate models for load and power output targets N. Dimitrov & A. Natarajan https://doi.org/10.1088/1742-6596/2018/1/012013
- Effects of turbulent inflow time scales on wind turbine wake behavior and recovery E. Hodgson et al. https://doi.org/10.1063/5.0162311
- A new method to characterize the curled wake shape under yaw misalignment B. Sengers et al. https://doi.org/10.1088/1742-6596/1618/6/062050
- Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems D. Song et al. https://doi.org/10.3390/jmse12030424
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. https://doi.org/10.1002/we.2852
- Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. https://doi.org/10.1088/1742-6596/2265/3/032074
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- Quantum-inspired multi-agent reinforcement learning and evolutionary optimization for stochastic wind farm control K. Cheikh et al. https://doi.org/10.1007/s43937-026-00133-8
- The characteristics of helically deflected wind turbine wakes H. Korb et al. https://doi.org/10.1017/jfm.2023.390
- A multi-fidelity model intercomparison for wake steering of a large turbine in a conventionally neutral atmospheric boundary layer J. Steiner et al. https://doi.org/10.5194/wes-11-1679-2026
- A bin-wise approach to wake steering optimization under fatigue load constraints I. Gharbia et al. https://doi.org/10.1088/1742-6596/3025/1/012017
- Turbulence-aware inflow feature engineering for physics-guided surrogate modeling of wind turbine dynamics Z. Liu et al. https://doi.org/10.1016/j.renene.2026.125840
- Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao https://doi.org/10.1109/TII.2021.3093300
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Impact of freestream turbulence integral length scale on wind farm flows and power generation E. Hodgson et al. https://doi.org/10.1016/j.renene.2024.121804
- Floating Offshore Wind Energy: Challenges and Research Needs in Fluid Mechanics A. Viré et al. https://doi.org/10.1007/s10494-025-00717-9
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. https://doi.org/10.1049/rpg2.12160
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. https://doi.org/10.5194/wes-7-1791-2022
- Multi-objective Bayesian optimisation over sparse subspaces for model predictive control of wind farms K. Hoang et al. https://doi.org/10.1016/j.renene.2025.122988
- Review of wake management techniques for wind turbines D. Houck https://doi.org/10.1002/we.2668
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. https://doi.org/10.5194/wes-7-1455-2022
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. https://doi.org/10.5194/wes-7-2181-2022
- Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al. https://doi.org/10.1088/1742-6596/2767/3/032049
- Time-Accurate Wind Farm Control in Dynamic Flow Conditions using Deep Reinforcement Learning H. Sheehan et al. https://doi.org/10.1088/1742-6596/3016/1/012025
- Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H. Dong et al. https://doi.org/10.1016/j.apenergy.2021.116928
- Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. https://doi.org/10.5194/wes-7-1941-2022
- Combining wake redirection and derating strategies in a load-constrained wind farm power maximization A. Croce et al. https://doi.org/10.5194/wes-9-1211-2024
- Maximizing wind farm production through pitch control using graph neural networks and hybrid learning methods Y. Huo et al. https://doi.org/10.1049/rpg2.13133
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. https://doi.org/10.3390/en14051293
- Wake steering optimization under multiple cumulative fatigue load constraints I. Gharbia et al. https://doi.org/10.1088/1742-6596/3224/3/032086
- Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. https://doi.org/10.1038/s41560-022-01085-8
- Real-time optimization of wind farms using modifier adaptation and machine learning L. Andersson & L. Imsland https://doi.org/10.5194/wes-5-885-2020
- An analytical modeling study on yaw-based wake redirection control for large-scale offshore wind farm annual energy power improvement J. Tan et al. https://doi.org/10.1063/5.0207111
- Uncertainty of toggling in wake steering experiments under diurnal cycle atmospheric conditions: an LES study E. Hodgson et al. https://doi.org/10.1088/1742-6596/3016/1/012026
- Multi-fidelity vortex simulations of rotor flows: Validation against detailed wake measurements N. Ramos-García et al. https://doi.org/10.1016/j.compfluid.2023.105790
- LES verification of HAWC2Farm aeroelastic wind farm simulations with wake steering and load analysis J. Liew et al. https://doi.org/10.1088/1742-6596/2265/2/022069
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. https://doi.org/10.1088/1742-6596/2265/3/032110
- Robust wake steering control design in a wind farm for power optimisation using adaptive learning game theory (ALGT) method V. Fazlollahi et al. https://doi.org/10.1080/00207179.2021.2009558
- A Semi-decentralized Data-Model-Driven Optimization Scheme for Coordinated Control of Large-Scale Wind Farm Power Maximization J. Hu et al. https://doi.org/10.1109/TII.2025.3606935
- Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur https://doi.org/10.3390/app12084013
- An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction Y. Wang et al. https://doi.org/10.1016/j.energy.2025.139116
- Wake steering under inflow wind direction uncertainty: an LES study E. Hodgson & S. Andersen https://doi.org/10.5194/wes-11-2173-2026
- Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution U. Ciri et al. https://doi.org/10.1071/WF20149
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. https://doi.org/10.1038/s41560-024-01516-8
- Dynamic interaction of inflow and rotor time scales and impact on single turbine wake recovery S. Andersen et al. https://doi.org/10.1088/1742-6596/2767/9/092002
- Quantifying the uncertain impact of wind turbine yaw misalignment on power production in a field experiment W. Hector & M. Howland https://doi.org/10.1063/5.0311885
- Impact of Turbulent Time Scales on Wake Recovery and Operation E. Hodgson et al. https://doi.org/10.1088/1742-6596/2265/2/022022
47 citations as recorded by crossref.
- Wind farm set point optimization with surrogate models for load and power output targets N. Dimitrov & A. Natarajan https://doi.org/10.1088/1742-6596/2018/1/012013
- Effects of turbulent inflow time scales on wind turbine wake behavior and recovery E. Hodgson et al. https://doi.org/10.1063/5.0162311
- A new method to characterize the curled wake shape under yaw misalignment B. Sengers et al. https://doi.org/10.1088/1742-6596/1618/6/062050
- Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems D. Song et al. https://doi.org/10.3390/jmse12030424
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. https://doi.org/10.1002/we.2852
- Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. https://doi.org/10.1088/1742-6596/2265/3/032074
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- Quantum-inspired multi-agent reinforcement learning and evolutionary optimization for stochastic wind farm control K. Cheikh et al. https://doi.org/10.1007/s43937-026-00133-8
- The characteristics of helically deflected wind turbine wakes H. Korb et al. https://doi.org/10.1017/jfm.2023.390
- A multi-fidelity model intercomparison for wake steering of a large turbine in a conventionally neutral atmospheric boundary layer J. Steiner et al. https://doi.org/10.5194/wes-11-1679-2026
- A bin-wise approach to wake steering optimization under fatigue load constraints I. Gharbia et al. https://doi.org/10.1088/1742-6596/3025/1/012017
- Turbulence-aware inflow feature engineering for physics-guided surrogate modeling of wind turbine dynamics Z. Liu et al. https://doi.org/10.1016/j.renene.2026.125840
- Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao https://doi.org/10.1109/TII.2021.3093300
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Impact of freestream turbulence integral length scale on wind farm flows and power generation E. Hodgson et al. https://doi.org/10.1016/j.renene.2024.121804
- Floating Offshore Wind Energy: Challenges and Research Needs in Fluid Mechanics A. Viré et al. https://doi.org/10.1007/s10494-025-00717-9
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. https://doi.org/10.1049/rpg2.12160
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. https://doi.org/10.5194/wes-7-1791-2022
- Multi-objective Bayesian optimisation over sparse subspaces for model predictive control of wind farms K. Hoang et al. https://doi.org/10.1016/j.renene.2025.122988
- Review of wake management techniques for wind turbines D. Houck https://doi.org/10.1002/we.2668
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. https://doi.org/10.5194/wes-7-1455-2022
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. https://doi.org/10.5194/wes-7-2181-2022
- Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al. https://doi.org/10.1088/1742-6596/2767/3/032049
- Time-Accurate Wind Farm Control in Dynamic Flow Conditions using Deep Reinforcement Learning H. Sheehan et al. https://doi.org/10.1088/1742-6596/3016/1/012025
- Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H. Dong et al. https://doi.org/10.1016/j.apenergy.2021.116928
- Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. https://doi.org/10.5194/wes-7-1941-2022
- Combining wake redirection and derating strategies in a load-constrained wind farm power maximization A. Croce et al. https://doi.org/10.5194/wes-9-1211-2024
- Maximizing wind farm production through pitch control using graph neural networks and hybrid learning methods Y. Huo et al. https://doi.org/10.1049/rpg2.13133
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. https://doi.org/10.3390/en14051293
- Wake steering optimization under multiple cumulative fatigue load constraints I. Gharbia et al. https://doi.org/10.1088/1742-6596/3224/3/032086
- Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. https://doi.org/10.1038/s41560-022-01085-8
- Real-time optimization of wind farms using modifier adaptation and machine learning L. Andersson & L. Imsland https://doi.org/10.5194/wes-5-885-2020
- An analytical modeling study on yaw-based wake redirection control for large-scale offshore wind farm annual energy power improvement J. Tan et al. https://doi.org/10.1063/5.0207111
- Uncertainty of toggling in wake steering experiments under diurnal cycle atmospheric conditions: an LES study E. Hodgson et al. https://doi.org/10.1088/1742-6596/3016/1/012026
- Multi-fidelity vortex simulations of rotor flows: Validation against detailed wake measurements N. Ramos-García et al. https://doi.org/10.1016/j.compfluid.2023.105790
- LES verification of HAWC2Farm aeroelastic wind farm simulations with wake steering and load analysis J. Liew et al. https://doi.org/10.1088/1742-6596/2265/2/022069
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. https://doi.org/10.1088/1742-6596/2265/3/032110
- Robust wake steering control design in a wind farm for power optimisation using adaptive learning game theory (ALGT) method V. Fazlollahi et al. https://doi.org/10.1080/00207179.2021.2009558
- A Semi-decentralized Data-Model-Driven Optimization Scheme for Coordinated Control of Large-Scale Wind Farm Power Maximization J. Hu et al. https://doi.org/10.1109/TII.2025.3606935
- Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur https://doi.org/10.3390/app12084013
- An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction Y. Wang et al. https://doi.org/10.1016/j.energy.2025.139116
- Wake steering under inflow wind direction uncertainty: an LES study E. Hodgson & S. Andersen https://doi.org/10.5194/wes-11-2173-2026
- Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution U. Ciri et al. https://doi.org/10.1071/WF20149
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. https://doi.org/10.1038/s41560-024-01516-8
- Dynamic interaction of inflow and rotor time scales and impact on single turbine wake recovery S. Andersen et al. https://doi.org/10.1088/1742-6596/2767/9/092002
- Quantifying the uncertain impact of wind turbine yaw misalignment on power production in a field experiment W. Hector & M. Howland https://doi.org/10.1063/5.0311885
- Impact of Turbulent Time Scales on Wake Recovery and Operation E. Hodgson et al. https://doi.org/10.1088/1742-6596/2265/2/022022
Saved (final revised paper)
Latest update: 14 Sep 2026
Short summary
We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high-fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The...
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