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Prediction of power generation of two 30 kW Horizontal Axis Wind Turbines with Gaussian model

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  • Li, Qing'an
  • Cai, Chang
  • Kamada, Yasunari
  • Maeda, Takao
  • Hiromori, Yuto
  • Zhou, Shuni
  • Xu, Jianzhong

Abstract

In order to improve the total efficiency of power generation in a wind farm, this paper investigated the effect of different operating conditions of the upstream wind turbine on the power of the downstream wind turbine by Gaussian wake model. A three-bladed Horizontal Axis Wind Turbine with the generator capacity of 30 kW was used. Firstly, Gaussian model for evaluating the wake flow was developed, which fit well with experiment data. Then, the influence of the operating condition of the upstream turbine on the downstream turbine performance was estimated by calculating the energy amount resulted from the velocity deficit of the wake. Finally, the effect of the installation position of the downstream wind turbine was analyzed. In consequence, the total power coefficient Ptotal of the wind turbines was relatively higher when the upstream turbine was at tip speed ratio λ = 6.4 and pitch angle β = 2° among different test conditions. Meanwhile, the Ptotal showed the maximum value when the downstream turbine was installed at the streamwise location x/D = 2.0 and lateral offset location y/R = 1.5 when the inflow turbulence intensity is 0.22≤TIref ≤ 0.35. This paper provided a better guidance for optimizing the wind turbine layout and improving the total power output of the entire wind farm.

Suggested Citation

  • Li, Qing'an & Cai, Chang & Kamada, Yasunari & Maeda, Takao & Hiromori, Yuto & Zhou, Shuni & Xu, Jianzhong, 2021. "Prediction of power generation of two 30 kW Horizontal Axis Wind Turbines with Gaussian model," Energy, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221013232
    DOI: 10.1016/j.energy.2021.121075
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    References listed on IDEAS

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    Cited by:

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    2. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    3. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
    4. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).

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