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Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines

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Listed:
  • Sun, Jili
  • Chen, Zheng
  • Yu, Hao
  • Gao, Shan
  • Wang, Bin
  • Ying, You
  • Sun, Yong
  • Qian, Peng
  • Zhang, Dahai
  • Si, Yulin

Abstract

Wind turbines in an offshore wind farm are usually allocated in a restricted area, which will result in wake interactions for downstream turbines. In order to mitigate wake effects on power and loads, wake redirection control (WRC) has been proposed to steer wake away from downstream turbines by intentionally yawing the upstream ones. However, wake interactions in combination with yaw-misalignment would produce complex structural loading behaviours, which should be carefully evaluated in control design. In this respect, priori knowledge on fatigue load contribution from wake and yaw-offset could be beneficial for multi-objective control optimisation and its real-time implementation. Therefore, we aim to quantitatively evaluate both yaw-offset and wake induced fatigue loads on offshore wind turbines in this work. More specifically, a numerical turbulent wind-field generator is established by including the wake deficit modelling feature, so that aero-elastic simulation with parametrically controlled wake inflow becomes possible. Then, aero-elastic simulations covering a wide range of waked inflow and yaw-offset conditions are performed, establishing a comprehensive database for tower and blade damage equivalent loads, where different load trends can be observed. In addition, a load assessment model is established by polynomial regression, and correlation analysis results indicate the dominant factors on fatigue loads are wind velocity and turbulence intensity. Besides, yaw-offset is more important than wakes for rated conditions, and vice versa for below and above-rated conditions. Moreover, wind tunnel results have also shown generally consistent trends with model predictions. The established load database and regression model could be used as the load indicator for future design optimisation of wind farm WRC.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:199:y:2022:i:c:p:71-86
    DOI: 10.1016/j.renene.2022.08.137
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    as
    1. Fei, Zhao & Tengyuan, Wang & Xiaoxia, Gao & Haiying, Sun & Hongxing, Yang & Zhonghe, Han & Yu, Wang & Xiaoxun, Zhu, 2020. "Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm," Energy, Elsevier, vol. 199(C).
    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. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Wen, Binrong & Tian, Xinliang & Dong, Xingjian & Peng, Zhike & Zhang, Wenming & Wei, Kexiang, 2019. "A numerical study on the angle of attack to the blade of a horizontal-axis offshore floating wind turbine under static and dynamic yawed conditions," Energy, Elsevier, vol. 168(C), pages 1138-1156.
    5. 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.
    6. Eidi, Ali & Ghiassi, Reza & Yang, Xiang & Abkar, Mahdi, 2021. "Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms," Renewable Energy, Elsevier, vol. 179(C), pages 2212-2223.
    7. Xu Ning & Decheng Wan, 2019. "LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics," Sustainability, MDPI, vol. 11(24), pages 1-26, December.
    8. Yin, Xiuxing & Zhang, Wencan & Jiang, Zhansi & Pan, Li, 2020. "Data-driven multi-objective predictive control of offshore wind farm based on evolutionary optimization," Renewable Energy, Elsevier, vol. 160(C), pages 974-986.
    9. Charlotte Bay Hasager & Nicolai Gayle Nygaard & Patrick J. H. Volker & Ioanna Karagali & Søren Juhl Andersen & Jake Badger, 2017. "Wind Farm Wake: The 2016 Horns Rev Photo Case," Energies, MDPI, vol. 10(3), pages 1-24, March.
    10. Brogna, Roberto & Feng, Ju & Sørensen, Jens Nørkær & Shen, Wen Zhong & Porté-Agel, Fernando, 2020. "A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain," Applied Energy, Elsevier, vol. 259(C).
    11. Johnston, Barry & Foley, Aoife & Doran, John & Littler, Timothy, 2020. "Levelised cost of energy, A challenge for offshore wind," Renewable Energy, Elsevier, vol. 160(C), pages 876-885.
    12. 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.
    13. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    14. González-Longatt, F. & Wall, P. & Terzija, V., 2012. "Wake effect in wind farm performance: Steady-state and dynamic behavior," Renewable Energy, Elsevier, vol. 39(1), pages 329-338.
    15. Wen, Binrong & Jiang, Zhihao & Li, Zhanwei & Peng, Zhike & Dong, Xingjian & Tian, Xinliang, 2022. "On the aerodynamic loading effect of a model Spar-type floating wind turbine: An experimental study," Renewable Energy, Elsevier, vol. 184(C), pages 306-319.
    16. Bashirzadeh Tabrizi, Amir & Whale, Jonathan & Lyons, Thomas & Urmee, Tania & Peinke, Joachim, 2017. "Modelling the structural loading of a small wind turbine at a highly turbulent site via modifications to the Kaimal turbulence spectra," Renewable Energy, Elsevier, vol. 105(C), pages 288-300.
    17. Na, Ji Sung & Koo, Eunmo & Muñoz-Esparza, Domingo & Jin, Emilia Kyung & Linn, Rodman & Lee, Joon Sang, 2016. "Turbulent kinetics of a large wind farm and their impact in the neutral boundary layer," Energy, Elsevier, vol. 95(C), pages 79-90.
    18. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
    19. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    20. Wei, Youzhou & Zou, Qing-Ping & Lin, Xianghong, 2021. "Evolution of price policy for offshore wind energy in China: Trilemma of capacity, price and subsidy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
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    Cited by:

    1. 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).

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