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Three-dimensional yaw wake model development with validations from wind tunnel experiments

Author

Listed:
  • He, Ruiyang
  • Deng, Xiaowei
  • Li, Yichun
  • Dong, Zhikun
  • Gao, Xiaoxia
  • Lu, Lin
  • Zhou, Yue
  • Wu, Jianzhong
  • Yang, Hongxing

Abstract

The presence of wake flows caused by wind turbines (WTs) diminish the expected power generation of wind energy and exacerbate structural vibrations. To mitigate these issues, yaw control has emerged as a promising technique for intentionally deflecting the wake away from downstream WTs. Consequently, accurate prediction of the yawed wake is of paramount importance for effective implementation of yaw control strategies. This study presents an innovative and comprehensive approach to modeling yaw wake behavior by introducing an advanced three-dimensional yaw wake model. This model incorporates anisotropic and general expressions of the wake expansion rate, allowing for a more accurate and physically meaningful representation of wake evolution. More importantly, the easily-available parameters in the function guarantee the generalization capability of the proposed model. Subsequently, a wake deflection mode is developed and integrated into the yaw wake model through the inclusion of a deflection term. To validate the proposed models, two sources of data are utilized. Firstly, well-known public measurements are used to verify the accuracy and reliability of the model predictions. Secondly, wind tunnel experiments are conducted by the authors, employing a particle image velocimetry (PIV) system to capture detailed flow field information. This combination of validation sources ensures a comprehensive assessment of the proposed models. The physical description and error analysis conducted in this study reveals that the proposed model outperforms other models in terms of predicting wake distribution and the trajectory of the deflected wake centreline. In particular, the comparative analysis confirms its superior performance in the main angle and downstream region that are of particular interest for active yaw control. The accurate and cost-efficient nature of the proposed analytical yaw wake model holds great potential for optimizing yaw control strategies in wind farms. This study is expected to contribute to the field by offering a reliable and practical tool for understanding and managing the effects of yaw operation on wake behavior.

Suggested Citation

  • He, Ruiyang & Deng, Xiaowei & Li, Yichun & Dong, Zhikun & Gao, Xiaoxia & Lu, Lin & Zhou, Yue & Wu, Jianzhong & Yang, Hongxing, 2023. "Three-dimensional yaw wake model development with validations from wind tunnel experiments," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223017966
    DOI: 10.1016/j.energy.2023.128402
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    References listed on IDEAS

    as
    1. 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).
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    4. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
    5. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    6. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    7. 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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