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Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control

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  • He, Ruiyang
  • Yang, Hongxing
  • Lu, Lin

Abstract

Yaw control is one of the most important farm-level active control strategies with the aim of power maximization and load mitigation. However, improper yaw strategies may induce a significant load increase on wind turbines (WTs) although a power enhancement can be achieved. Therefore, it is essential to conduct a comprehensive fatigue analysis of wind turbines under yaw control before proposing an optimal yaw control strategy. In this study, fatigue analysis of WTs under shear flow is conducted first under various environmental and yaw conditions. Bending moments at not only blade root and yaw bearing, but also non-rotating component like tower base are investigated. Furthermore, various wake conditions including both full wake and partial wake are generated to explore the combined effects of wake flow and yaw control. Different load trends and variation rates are obtained in the analysis. To determine the optimal yaw angle at different practical inflow and yaw conditions, a cost function is proposed by using resultant bending moments and further integrating loads at different components together. The results show that efficient yaw ranges all appear in the positive yaw direction and optimal yaw angle increases with the increase of inflow speeds. The comprehensive fatigue analysis and the proposed cost function for optimal yaw control in this work are expected to contribute to the research and applications of multi-objective active yaw control strategies and therefore to increase total power output while extending operation lifetime of wind turbines.

Suggested Citation

  • He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:appene:v:337:y:2023:i:c:s0306261923002428
    DOI: 10.1016/j.apenergy.2023.120878
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    References listed on IDEAS

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    1. Sun, Jili & Chen, Zheng & Yu, Hao & Gao, Shan & Wang, Bin & Ying, You & Sun, Yong & Qian, Peng & Zhang, Dahai & Si, Yulin, 2022. "Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines," Renewable Energy, Elsevier, vol. 199(C), pages 71-86.
    2. van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    3. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    4. Shen, Xin & Zhu, Xiaocheng & Du, Zhaohui, 2011. "Wind turbine aerodynamics and loads control in wind shear flow," Energy, Elsevier, vol. 36(3), pages 1424-1434.
    5. 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).
    6. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    7. Luna, Julio & Falkenberg, Ole & Gros, Sébastien & Schild, Axel, 2020. "Wind turbine fatigue reduction based on economic-tracking NMPC with direct ANN fatigue estimation," Renewable Energy, Elsevier, vol. 147(P1), pages 1632-1641.
    8. 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).
    9. Jeong, Min-Soo & Kim, Sang-Woo & Lee, In & Yoo, Seung-Jae & Park, K.C., 2013. "The impact of yaw error on aeroelastic characteristics of a horizontal axis wind turbine blade," Renewable Energy, Elsevier, vol. 60(C), pages 256-268.
    10. Chanprasert, W. & Sharma, R.N. & Cater, J.E. & Norris, S.E., 2022. "Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence," Renewable Energy, Elsevier, vol. 190(C), pages 208-222.
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