Articles | Volume 6, issue 3
https://doi.org/10.5194/wes-6-701-2021
© Author(s) 2021. 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-6-701-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 May 2021
Research article |
| 21 May 2021
| 21 May 2021
Control-oriented model for secondary effects of wake steering
Jennifer King
CORRESPONDING AUTHOR
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
Ryan King
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
Luis A. Martínez-Tossas
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
Christopher J. 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
Eric Simley
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
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- Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. https://doi.org/10.1063/5.0129481
- Data–Driven Wake Steering Control for a Simulated Wind Farm Model S. Simani et al. https://doi.org/10.31875/2409-9694.2023.10.02
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- A three-dimensional, analytical wind turbine wake model: Flow acceleration, empirical correlations, and continuity Z. Sadek et al. https://doi.org/10.1016/j.renene.2023.03.129
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- Effectiveness of cooperative yaw control based on reinforcement learning for in-line multiple wind turbines L. Wang et al. https://doi.org/10.1016/j.conengprac.2024.106124
- Wake Control Method of Wind Farm Considering Yaw Error S. Zhu et al. https://doi.org/10.1061/JLEED9.EYENG-6068
- Investigation of wake steering control effects on the dynamic responses of 15 MW semi-submersible floating wind farms T. Zhang et al. https://doi.org/10.1016/j.renene.2025.123704
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- LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment J. Bohrer et al. https://doi.org/10.1088/1742-6596/2767/3/032041
- 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
- Scalable SCADA-Based Calibration for Analytical Wake Models Across an Offshore Cluster D. Binsbergen et al. https://doi.org/10.1088/1742-6596/2745/1/012014
- Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. https://doi.org/10.1109/TCST.2024.3362518
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- Multi-fidelity Bayesian Optimisation of Wind Farm Wake Steering using Wake Models and Large Eddy Simulations A. Mole & S. Laizet https://doi.org/10.1007/s10494-024-00629-0
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- Wind plant wake losses: Disconnect between turbine actuation and control of plant wakes with engineering wake models R. Scott et al. https://doi.org/10.1063/5.0207013
- The value of wake steering wind farm flow control in US energy markets E. Simley et al. https://doi.org/10.5194/wes-9-219-2024
- Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind Farms D. Zhang et al. https://doi.org/10.3390/en18112897
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- Achieving Power-Noise Balance in Wind Farms by Fine-Tuning the Layout with Reinforcement Learning G. Guo et al. https://doi.org/10.3390/en18185019
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- Dynamic performance of a passively self-adjusting floating wind farm layout to increase the annual energy production M. Mahfouz et al. https://doi.org/10.5194/wes-9-1595-2024
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- A comparison between scanning LiDAR and scada-based hyperparameter tuning of analytical wake models P. Daems et al. https://doi.org/10.1088/1742-6596/3025/1/012002
- Optimizing the Total Power Output in a Wind Farm Using Long-Short Term Memory B. Namlı et al. https://doi.org/10.3390/su18105155
- Development and validation of a hybrid data-driven model-based wake steering controller and its application at a utility-scale wind plant P. Bachant et al. https://doi.org/10.5194/wes-9-2235-2024
- A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. https://doi.org/10.1017/jfm.2021.1010
- Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. https://doi.org/10.5194/wes-9-721-2024
- A three-dimensional dynamic wake prediction framework for multiple turbine operating states based on diffusion model M. Song et al. https://doi.org/10.1016/j.energy.2025.137084
- Deep reinforcement learning-based adaptive yaw control for wind farms in fluctuating winds Q. Dong et al. https://doi.org/10.1063/5.0267200
- Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. https://doi.org/10.1002/we.2953
- Integrated wind farm design: optimizing turbine placement and cable routing with wake effects J. Pedersen et al. https://doi.org/10.1007/s00291-026-00862-1
- Flow in a large wind field with multiple actuators in the presence of constant vorticity S. Basu et al. https://doi.org/10.1063/5.0104902
- Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model C. Bay et al. https://doi.org/10.5194/wes-8-401-2023
- Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering Y. Liu et al. https://doi.org/10.1016/j.energy.2024.132900
- Surrogate-assisted power optimization framework for heterogeneous Wind farms under turbine upgrade scenarios C. Zhao et al. https://doi.org/10.1016/j.apenergy.2026.127813
- A fully three-dimensional deep learning surrogate model for large-eddy simulation-resolved wake dynamics in wind farm M. Ahn et al. https://doi.org/10.1063/5.0330691
- Active Wake Steering Control Data-Driven Design for a Wind Farm Benchmark S. Simani et al. https://doi.org/10.1016/j.ifacol.2023.10.1504
- Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. https://doi.org/10.3390/en16093676
- Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations N. Bempedelis et al. https://doi.org/10.5194/wes-9-869-2024
- The fluid mechanics of active flow control at very large scales C. Meneveau https://doi.org/10.1017/jfm.2024.846
- Experimental results of wake steering using fixed angles P. Fleming et al. https://doi.org/10.5194/wes-6-1521-2021
- Integrated floating wind farm layout design and mooring system optimization to increase annual energy production M. Mahfouz et al. https://doi.org/10.1088/1742-6596/2767/6/062020
- Positive effect of wind farm control on energy production and revenue in European markets F. Wasilczuk et al. https://doi.org/10.1016/j.energy.2025.136159
- 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
- An interpretable data-driven intelligent prediction framework for wind turbine wake turbulence intensity based on machine learning model Z. Li et al. https://doi.org/10.1016/j.engstruct.2026.122916
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. https://doi.org/10.3390/en15061964
- A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha https://doi.org/10.1115/1.4054501
- Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. https://doi.org/10.5194/wes-6-1427-2021
- Trailing-Edge Noise and Amplitude Modulation Under Yaw-Induced Partial Wake: A Curl–UVLM Analysis with Atmospheric Stability Effects H. Kim et al. https://doi.org/10.3390/en18195205
- Characterizing Wake Behavior of Adaptive Aerodynamic Structures Using Reduced-Order Models K. Sadeghilari et al. https://doi.org/10.3390/en18143648
- Bi-level multi-objective optimization framework for wake escape in floating offshore wind farm C. Huang et al. https://doi.org/10.1016/j.apenergy.2024.124712
- Structural control of floating offshore wind turbines via active yaw control Y. Wang & Z. Liu https://doi.org/10.1016/j.jfluidstructs.2025.104462
- A nonlinear wake model of a wind turbine considering the yaw wake steering Y. Li et al. https://doi.org/10.1007/s00343-023-3040-6
- Improving Floating Offshore Wind Farm Flow Control With Scalable Model-Based Deep Reinforcement Learning M. Mei et al. https://doi.org/10.1109/TASE.2025.3585016
- The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi https://doi.org/10.1115/1.4052775
- 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
- Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol https://doi.org/10.1063/5.0107244
- Overview of recent observations and simulations from the American WAKE experimeNt (AWAKEN) field campaign P. Moriarty et al. https://doi.org/10.1088/1742-6596/2505/1/012049
- Advancements in Wind Farm Control: Modelling and Multi-Objective Optimization Through Yaw-Based Wake Steering T. Lucas Frutuoso et al. https://doi.org/10.3390/en18092247
- Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control Y. Wang & Z. Liu https://doi.org/10.1016/j.energy.2025.136266
- Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. https://doi.org/10.1063/5.0129481
- Data–Driven Wake Steering Control for a Simulated Wind Farm Model S. Simani et al. https://doi.org/10.31875/2409-9694.2023.10.02
- Hybrid Offshore Wind Farm Wake Optimization with Multi-Type Wind Turbines C. Huang et al. https://doi.org/10.3390/jmse14070674
- A Comparative Analysis of Actuator-Based Turbine Structure Parametrizations for High-Fidelity Modeling of Utility-Scale Wind Turbines under Neutral Atmospheric Conditions C. Santoni et al. https://doi.org/10.3390/en17030753
- Global optimization of wake steering for large-scale wind farms using generalized serial refinement method Y. Tu et al. https://doi.org/10.1016/j.apenergy.2025.127259
- A three-dimensional, analytical wind turbine wake model: Flow acceleration, empirical correlations, and continuity Z. Sadek et al. https://doi.org/10.1016/j.renene.2023.03.129
- Integer programming for optimal yaw control of wind farms F. Bestehorn et al. https://doi.org/10.5194/wes-10-1637-2025
- Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. https://doi.org/10.1016/j.oceaneng.2024.117706
- 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
- On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al. https://doi.org/10.5194/wes-9-1547-2024
- Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon https://doi.org/10.5194/wes-9-495-2024
- Wind Farm Design with 15 MW Floating Offshore Wind Turbines in Typhoon Regions K. Ma et al. https://doi.org/10.3390/jmse13040687
- Effectiveness of cooperative yaw control based on reinforcement learning for in-line multiple wind turbines L. Wang et al. https://doi.org/10.1016/j.conengprac.2024.106124
- Wake Control Method of Wind Farm Considering Yaw Error S. Zhu et al. https://doi.org/10.1061/JLEED9.EYENG-6068
- Investigation of wake steering control effects on the dynamic responses of 15 MW semi-submersible floating wind farms T. Zhang et al. https://doi.org/10.1016/j.renene.2025.123704
- 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
- On the importance of wind predictions in wake steering optimization E. Kadoche et al. https://doi.org/10.5194/wes-9-1577-2024
- Aerodynamic characterization of two tandem wind turbines under yaw misalignment control using actuator line model Y. Tu et al. https://doi.org/10.1016/j.oceaneng.2023.114992
- Enhancing wind farm power output via DRL-based collaborative pitch control of tandem turbines L. Wang et al. https://doi.org/10.1016/j.energy.2026.140015
- Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. https://doi.org/10.1088/1742-6596/2265/2/022019
- LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment J. Bohrer et al. https://doi.org/10.1088/1742-6596/2767/3/032041
- 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
- Scalable SCADA-Based Calibration for Analytical Wake Models Across an Offshore Cluster D. Binsbergen et al. https://doi.org/10.1088/1742-6596/2745/1/012014
- Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. https://doi.org/10.1109/TCST.2024.3362518
- Brief communication: An elliptical parameterisation of the wind direction rose E. Hart https://doi.org/10.5194/wes-10-1821-2025
- Validation of induction/steering reserve-boosting active power control by a wind tunnel experiment with dynamic wind direction changes S. Tamaro et al. https://doi.org/10.1088/1742-6596/2767/9/092067
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. https://doi.org/10.5194/wes-7-1455-2022
- Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering A. Henry et al. https://doi.org/10.1109/TCST.2025.3639652
- A new physically based model for cumulative wakes and application to wind farms Z. Zhang et al. https://doi.org/10.1016/j.enconman.2026.121593
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. https://doi.org/10.5194/wes-8-1425-2023
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. https://doi.org/10.5194/wes-9-1507-2024
- Assessing Engineering Wake Models Against Operational Data: Insights From the Lillgrund Wind Farm Wake Steering Campaign D. Siguenza‐Alvarado et al. https://doi.org/10.1002/we.70126
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. https://doi.org/10.1063/5.0163896
- 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. https://doi.org/10.1016/j.jweia.2023.105559
- Stochastic black-box optimization using multi-fidelity score function estimator A. Agrawal et al. https://doi.org/10.1088/2632-2153/ad8e2b
- Study of three wake control strategies for power maximization of offshore wind farms with different layouts B. Li et al. https://doi.org/10.1016/j.enconman.2022.116059
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. https://doi.org/10.5194/wes-7-1791-2022
- Effectively using multifidelity optimization for wind turbine design J. Jasa et al. https://doi.org/10.5194/wes-7-991-2022
- Robust wind farm layout optimization M. Sinner & P. Fleming https://doi.org/10.1088/1742-6596/2767/3/032036
- A robust active power control algorithm to maximize wind farm power tracking margins in waked conditions S. Tamaro et al. https://doi.org/10.5194/wes-10-2705-2025
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Latest update: 28 May 2026
Short summary
This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
This paper highlights the secondary effects of wake steering, including yaw-added wake recovery...
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