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Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model

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  • Dou, Bingzheng
  • Qu, Timing
  • Lei, Liping
  • Zeng, Pan

Abstract

An appropriate yaw angle misalignment of the wind turbines in a wind farm has been found to improve the average energy production of the turbine array. Predicting the spatial evolution of the yawed turbine wakes is a key factor in optimizing the yaw angles. In this study, a new three-dimensional yawed wake model is proposed to estimate the non-centrosymmetric cross-sectional shape of the yawed wake velocity distribution, and the model is experimentally validated. Then, a yaw angle optimization strategy that optimizes the wind farm yaw angle distribution and maximizes the power output using the proposed wake model is described. The covariance matrix adaptation evolution strategy is employed as an intelligent algorithm to implement the optimization. The results indicate that yaw angle optimization improves the power of an offshore wind farm by up to 7%, and the optimization yields better results for a small streamwise spacing between turbines than for a large streamwise spacing. Wind farm yaw angle optimization shows great promise for the development of smart wind farms because it has the potential to enable real-time optimization of the yaw angles in response to changes in the incoming wind direction.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s036054422031522x
    DOI: 10.1016/j.energy.2020.118415
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    References listed on IDEAS

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    Cited by:

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    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.
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    11. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
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    13. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
    14. Anagnostopoulos, Sokratis J. & Bauer, Jens & Clare, Mariana C.A. & Piggott, Matthew D., 2023. "Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models," Renewable Energy, Elsevier, vol. 218(C).
    15. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    16. Chen, Zhenyu & Lin, Zhongwei & Zhai, Xiaoya & Liu, Jizhen, 2022. "Dynamic wind turbine wake reconstruction: A Koopman-linear flow estimator," Energy, Elsevier, vol. 238(PB).
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    18. 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).

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