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Wind farm cooperative control under unsteady inflow conditions considering dynamic wake interactions

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  • Yang, Shanghui
  • Deng, Xiaowei
  • Dai, Feng
  • Yang, Kun
  • Wang, Qiulei
  • Dong, Zhikun

Abstract

Overlooking wake dynamics undermines the real-time control performance of wind farms. This paper proposes a novel dynamic wind farm control framework that integrates a mid-fidelity dynamic wake model, the FLORIDyn model, with surrogate model optimization, the DYCORS algorithm, to achieve optimal coordinated control settings within the yaw update interval accurately and efficiently. The framework is tested in a 6-turbine wind farm exposed to time-varying inflow conditions over 2400 s, with the conventional steady framework as the comparison. Additionally, parametric studies on yaw update interval and wind variability are conducted to explore the applicability of the dynamic framework under different inflow and operation conditions. Results indicate that achieving the anticipated power output of the steady wind farm control framework is challenging in a realistic wind farm setting. The proposed dynamic wind farm control framework enhances the power benefits of wake redirection compared to the steady framework, achieving a 2.22 % increase in power gains. The dynamic optimal control is more sensitive to yaw update interval variations than the greedy control. A smaller Hurst exponent, indicating increased stationarity of the inflow condition, reduces the power disparities between steady and dynamic control optimizations. Directional variability imposes a more distinct impact on control benefits than speed variability.

Suggested Citation

  • Yang, Shanghui & Deng, Xiaowei & Dai, Feng & Yang, Kun & Wang, Qiulei & Dong, Zhikun, 2025. "Wind farm cooperative control under unsteady inflow conditions considering dynamic wake interactions," Renewable Energy, Elsevier, vol. 244(C).
  • Handle: RePEc:eee:renene:v:244:y:2025:i:c:s0960148125003167
    DOI: 10.1016/j.renene.2025.122654
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    References listed on IDEAS

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    1. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    2. Doekemeijer, Bart M. & van der Hoek, Daan & van Wingerden, Jan-Willem, 2020. "Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions," Renewable Energy, Elsevier, vol. 156(C), pages 719-730.
    3. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    4. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
    5. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    6. van der Hoek, Daan & Kanev, Stoyan & Allin, Julian & Bieniek, David & Mittelmeier, Niko, 2019. "Effects of axial induction control on wind farm energy production - A field test," Renewable Energy, Elsevier, vol. 140(C), pages 994-1003.
    7. Wilson, Dennis & Rodrigues, Silvio & Segura, Carlos & Loshchilov, Ilya & Hutter, Frank & Buenfil, Guillermo López & Kheiri, Ahmed & Keedwell, Ed & Ocampo-Pineda, Mario & Özcan, Ender & Peña, Sergio Iv, 2018. "Evolutionary computation for wind farm layout optimization," Renewable Energy, Elsevier, vol. 126(C), pages 681-691.
    8. Yang, Shanghui & Deng, Xiaowei & Ti, Zilong & Yan, Bowen & Yang, Qingshan, 2022. "Cooperative yaw control of wind farm using a double-layer machine learning framework," Renewable Energy, Elsevier, vol. 193(C), pages 519-537.
    9. Cai, Wei & Hu, Yang & Fang, Fang & Yao, Lujin & Liu, Jizhen, 2023. "Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines," Applied Energy, Elsevier, vol. 339(C).
    10. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    11. Yang, Shanghui & Deng, Xiaowei & Yang, Kun, 2024. "Machine-learning-based wind farm optimization through layout design and yaw control," Renewable Energy, Elsevier, vol. 224(C).
    12. Michael F. Howland & Jesús Bas Quesada & Juan José Pena Martínez & Felipe Palou Larrañaga & Neeraj Yadav & Jasvipul S. Chawla & Varun Sivaram & John O. Dabiri, 2022. "Collective wind farm operation based on a predictive model increases utility-scale energy production," Nature Energy, Nature, vol. 7(9), pages 818-827, September.
    13. Wim Munters & Johan Meyers, 2018. "Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization," Energies, MDPI, vol. 11(1), pages 1-32, January.
    14. Zong, Haohua & Porté-Agel, Fernando, 2021. "Experimental investigation and analytical modelling of active yaw control for wind farm power optimization," Renewable Energy, Elsevier, vol. 170(C), pages 1228-1244.
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