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A control-oriented large eddy simulation of wind turbine wake considering effects of Coriolis force and time-varying wind conditions

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  • Qian, Guo-Wei
  • Song, Yun-Peng
  • Ishihara, Takeshi

Abstract

A control-oriented large eddy simulation (LES) code is developed to predict wind turbine wake and is validated by laboratory-scale and utility-scale wind turbines. Firstly, the wind turbine control algorithms including torque, pitch and yaw controls are implemented in LES with Actuator Line Model (ALM). Two sets of numerical simulations under uniform inflow with time-varying wind speeds and wind directions are performed. The simulated thrust and torque forces agree well with those calculated by the aeroelastic code, FAST. The predicted mean velocity and turbulence intensity in the wake of a laboratory-scale wind turbine show favorable agreement with those measured in wind tunnel experiments. The blade rotation induced dynamic effects on wake flows and rotor loading are well reproduced by ALM. Finally, numerical simulations for a utility-scale wind turbine are conducted, in which the ambient flow filed with time-varying wind speeds and wind directions are generated based on the wind condition measured by the Met-mast and the Coriolis force effect is incorporated as well. The predicted time series of control signals and power production agree well with the wind turbine SCADA data and the predicted mean wind speed in the wake region show favorable agreement with those measured by Doppler scanning LiDAR.

Suggested Citation

  • Qian, Guo-Wei & Song, Yun-Peng & Ishihara, Takeshi, 2022. "A control-oriented large eddy simulation of wind turbine wake considering effects of Coriolis force and time-varying wind conditions," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221021241
    DOI: 10.1016/j.energy.2021.121876
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    References listed on IDEAS

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    1. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
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    Cited by:

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    3. Xiaoxia, Gao & Luqing, Li & Shaohai, Zhang & Xiaoxun, Zhu & Haiying, Sun & Hongxing, Yang & Yu, Wang & Hao, Lu, 2022. "LiDAR-based observation and derivation of large-scale wind turbine's wake expansion model downstream of a hill," Energy, Elsevier, vol. 259(C).
    4. Zhiwen Deng & Chang Xu & Zhihong Huo & Xingxing Han & Feifei Xue, 2023. "Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model," Energies, MDPI, vol. 16(9), pages 1-20, May.
    5. 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).
    6. Wang, Tengyuan & Cai, Chang & Wang, Xinbao & Wang, Zekun & Chen, Yewen & Song, Juanjuan & Xu, Jianzhong & Zhang, Yuning & Li, Qingan, 2023. "A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow," Energy, Elsevier, vol. 271(C).
    7. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).

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