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A new three-dimensional analytical model for wind turbine wake turbulence intensity predictions

Author

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  • Tian, Linlin
  • Song, Yilei
  • Xiao, Pengcheng
  • Zhao, Ning
  • Shen, Wenzhong
  • Zhu, Chunling

Abstract

In wind farm design, accurately predicting the wake turbulence level is crucial for turbine power and load evaluation. However, a knowledge gap still exists on the characteristics of wake turbulence, meanwhile there has been little work on the development of related engineering models. In view of this, firstly, one-dimensional analytical models that can estimate the wake width and maximum wake turbulence level at any streamwise positions are proposed and validated. Based on these, a highly simple three-dimensional cosine shape (3D-COTI) model is proposed for estimating the wake turbulence intensity in an effective way. Moreover, by taking into account the wind shear and ground effects, this proposed model is capable of describing the anisotropic property of the 3-D wake field. Afterwards, model evaluations are performed through several test cases consisting various types of turbines operating under a wide range of inflow conditions. Overall, the proposed model shows a good agreement with the measurements about the spatial distribution of turbulence enhancement within the wake flow. Because of its good accuracy, simplicity and universality, the present model has potential for large-scale wind farm applications.

Suggested Citation

  • Tian, Linlin & Song, Yilei & Xiao, Pengcheng & Zhao, Ning & Shen, Wenzhong & Zhu, Chunling, 2022. "A new three-dimensional analytical model for wind turbine wake turbulence intensity predictions," Renewable Energy, Elsevier, vol. 189(C), pages 762-776.
    • Handle: RePEc:eee:renene:v:189:y:2022:i:c:p:762-776
    DOI: 10.1016/j.renene.2022.02.115
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    References listed on IDEAS

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    Cited by:

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    3. 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).
    4. Zhang, Shaohai & Gao, Xiaoxia & Ma, Wanli & Lu, Hongkun & Lv, Tao & Xu, Shinai & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu, 2023. "Derivation and verification of three-dimensional wake model of multiple wind turbines based on super-Gaussian function," Renewable Energy, Elsevier, vol. 215(C).
    5. 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).
    6. Zhang, Shaohai & Duan, Huanfeng & Lu, Lin & He, Ruiyang & Gao, Xiaoxia & Zhu, Songye, 2024. "Quantification of three-dimensional added turbulence intensity for the horizontal-axis wind turbine considering the wake anisotropy," Energy, Elsevier, vol. 294(C).

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