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A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow

Author

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  • Wang, Tengyuan
  • Cai, Chang
  • Wang, Xinbao
  • Wang, Zekun
  • Chen, Yewen
  • Song, Juanjuan
  • Xu, Jianzhong
  • Zhang, Yuning
  • Li, Qingan

Abstract

Wind turbine wake has a great effect on wind power generation and fatigue loads of downstream wind turbines in wind farms. Analytical engineering wake model is adopted to evaluate the power loss caused by turbine wake for the optimization of wind farm layout. Gaussian distribution is often employed in wake model. Traditional Gaussian distributions, on the other hand, are unable to accurately describe wind speed especially near the wake boundary. Besides, the variable turbulent environment in wind farms with complex terrain poses a challenge to the turbine wake model. In this paper, the unfitness of the Gaussian wake model especially near the wake boundary is addressed and a Gaussian wake model with new turbulence intensity model is proposed. Compared with previous models, this new wake model has a more suitable wind speed distribution. The new wake model is then validated using wind tunnel measurements under different turbulence intensity. Results indicate that the new Gaussian wake model can more correctly predict the wind turbine wake than the previous Gaussian wake models. The new Gaussian wake model suitable shows a good application prospect in wind farm layout optimization which improves the power generation and reduces the power generation costs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004838
    DOI: 10.1016/j.energy.2023.127089
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    References listed on IDEAS

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    Cited by:

    1. Kuichao Ma & Huanqiang Zhang & Xiaoxia Gao & Xiaodong Wang & Heng Nian & Wei Fan, 2024. "Research on Evaluation Method of Wind Farm Wake Energy Efficiency Loss Based on SCADA Data Analysis," Sustainability, MDPI, vol. 16(5), pages 1-16, February.

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