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Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test

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  • Gao, Xiaoxia
  • Zhang, Shaohai
  • Li, Luqing
  • Xu, Shinai
  • Chen, Yao
  • Zhu, Xiaoxun
  • Sun, Haiying
  • Wang, Yu
  • Lu, Hao

Abstract

To further understand the spatiotemporal inhomogeneity characteristics of wake profile caused by the inflow change of upstream wind turbine and reduce the influence of wake expansion on the power output and fatigue load of downstream wind turbine, a three-dimensional full wake model (3DJGF model) and a three-dimensional temporal wake model (3DJGF-T model) are proposed and verified in this paper. Assuming that the downstream wake distribution of wind turbine is double-Gaussian distribution, a new 3DJGF model is derived based on the law of conservation of mass and considering the wind shear effect. Based on the 3DJGF model, the time variable is introduced to calculate the delay time, and the 3DJGF-T model of the whole flow field is obtained. The accuracy of the proposed 3DJGF model and 3DJGF-T model is verified by wind field measurement and wind tunnel test, and the relative error of the 3DJGF-T model is analyzed: under the time-varying characteristics of different incoming wind speeds, the error of the newly proposed 3DJGF-T model is basically within 4%. The results show that the 3DJGF-T model can effectively describe the spatiotemporal inhomogeneity distribution and expansion characteristics of wake, which can provide a reference for the dynamic load analysis of downstream wind turbine considering the dynamic adjustment process of wind turbine control strategy, further improve the wake model and optimize the control strategy of wind turbine.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pa:s036054422201180x
    DOI: 10.1016/j.energy.2022.124277
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    7. 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).
    8. Wang, Tengyuan & Cai, Chang & Liu, Junbo & Peng, Chaoyi & Wang, Yibo & Sun, Xiangyu & Zhong, Xiaohui & Zhang, Jingjing & Li, Qingan, 2024. "Wake characteristics and vortex structure evolution of floating offshore wind turbine under surge motion," Energy, Elsevier, vol. 302(C).
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