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Wind turbine power prediction considering wake effects with dual laser beam LiDAR measured yaw misalignment

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  • Li, Xuyang
  • Qiu, Yingning
  • Feng, Yanhui
  • Wang, Zheng

Abstract

Accurate power prediction of a wind turbine under wake is important for wake suppression control, which is of great significance to reduce the energy loss of a wind farm. The power output of a wind turbine affected by wake is determined by yaw misalignment angle, equivalent inflow wind speed and the size of wake area imposed. However, with current techniques, it is difficult to measure the yaw misalignment angle of a wind turbine under wake that hinders the calculation of its equivalent inflow wind speed. By exploring the interference mechanism of the wake effects to the measurement of yaw misalignment angle with dual laser beam wind LiDAR an approach to calculate realistic yaw misalignment angle of a wind turbine under wake is proposed. Based on this and combined with the wake superposition model, the equivalent inflow wind speed of the wind turbine under wake can be determined. This enables a model to predict the power output of a wind turbine under wake effects. The model is verified by comparisons between prediction results and the data collected in Supervisory Control And Data Acquisition system from a wind farm with five inline wind turbines. It shows that with the wake compensation, the predicted equivalent wind speeds have achieved higher than 90% of accuracy and the output power predictions have maximum 7% accuracy improvements. The proposed calculation method of yaw misalignment angle with wind LiDAR and the power output prediction model of wind turbines under wake provide important basis for wind farm wake control.

Suggested Citation

  • Li, Xuyang & Qiu, Yingning & Feng, Yanhui & Wang, Zheng, 2021. "Wind turbine power prediction considering wake effects with dual laser beam LiDAR measured yaw misalignment," Applied Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:appene:v:299:y:2021:i:c:s0306261921007200
    DOI: 10.1016/j.apenergy.2021.117308
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    References listed on IDEAS

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    Cited by:

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    5. 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).
    6. Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
    7. Esmail Mahmoodi & Mohammad Khezri & Arash Ebrahimi & Uwe Ritschel & Leonardo P. Chamorro & Ali Khanjari, 2023. "A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines," Energies, MDPI, vol. 16(15), pages 1-13, July.
    8. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    9. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    10. Zhou, Huanyu & Qiu, Yingning & Feng, Yanhui & Liu, Jing, 2022. "Power prediction of wind turbine in the wake using hybrid physical process and machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 568-586.

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