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Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation

Author

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  • Dongqin Zhang

    (School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China)

  • Yang Liang

    (School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China)

  • Chao Li

    (School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China)

  • Yiqing Xiao

    (School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China)

  • Gang Hu

    (School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China
    Shenzhen Key Laboratory of Intelligent Structure System in Civil Engineering, Harbin Institute of Technology, Shenzhen 518055, China
    Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, Harbin Institute of Technology, Shenzhen 518055, China)

Abstract

Turbine-induced velocity deficit is the main reason to reduce wind farm power generation and increase the fatigue loadings. It is meaningful to investigate turbine-induced wake structures by a simple and accurate method. In this study, a series of single turbine wake models are proposed by combining different spanwise distributions and wake boundary expansion models. It is found that several combined wake models with high hit rates are more accurate and universal. Subsequently, the wake models for multiple wind turbines are also investigated by considering the combined wake models for single turbine and proper superposition approaches. Several excellent plans are provided where the velocity, turbulence intensity, and wind power generation for multiple wind turbines can be accurately evaluated. Finally, effects of thrust coefficient and ambient turbulence intensity are studied. In summary, the combined wake models for both single and multiple wind turbines are proposed and validated, enhancing the precision of wind farm layout optimization will be helped by using these wake models.

Suggested Citation

  • Dongqin Zhang & Yang Liang & Chao Li & Yiqing Xiao & Gang Hu, 2022. "Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation," Energies, MDPI, vol. 15(19), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7431-:d:938191
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    References listed on IDEAS

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