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Quantification of 4D spatial-temporal inhomogeneous added turbulence intensity in wake region with validations from LiDAR-based observation

Author

Listed:
  • Gao, Xiaoxia
  • Hu, Yingjun
  • Zhao, Fei
  • Chen, Hanye
  • Zhu, Xiaoxun
  • Yin, Qianqian
  • Wang, Yu

Abstract

Due to the large size of wind turbines and densification of wind turbines’ distribution, accurately predicting the wake turbulence intensity (TI) is essential for safeguarding the safety of wind farms and ensuring maximum power output. However, there are few analytical models compared to the wake velocity model. To solve this, this study aims to develop a new TI model involving spatial and temporal distribution. Firstly, added TI model considering time variation is proposed based on the previous model. Secondly, a delay time model is derived and applied in the new proposed added TI model to improve its accuracy. Finally, LES data and field experiment data were used to verify accuracy of the newly proposed added TI model, and the affecting factors in this model are analyzed with a sinusoidal inflow condition. The results show that the model fits well with experimental data and can better reflect the dynamic characteristics of the TI in the actual wind farm, especially in the environments where wind speed changes frequently. This model can provide accurate spatial-temporal distribution of TI for the design of control strategies and avoid premature intervention of control commands.

Suggested Citation

  • Gao, Xiaoxia & Hu, Yingjun & Zhao, Fei & Chen, Hanye & Zhu, Xiaoxun & Yin, Qianqian & Wang, Yu, 2025. "Quantification of 4D spatial-temporal inhomogeneous added turbulence intensity in wake region with validations from LiDAR-based observation," Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:energy:v:339:y:2025:i:c:s0360544225046948
    DOI: 10.1016/j.energy.2025.139052
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    References listed on IDEAS

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    1. Shives, Michael & Crawford, Curran, 2016. "Adapted two-equation turbulence closures for actuator disk RANS simulations of wind & tidal turbine wakes," Renewable Energy, Elsevier, vol. 92(C), pages 273-292.
    2. Ren, Guorui & Liu, Jinfu & Wan, Jie & Li, Fei & Guo, Yufeng & Yu, Daren, 2018. "The analysis of turbulence intensity based on wind speed data in onshore wind farms," Renewable Energy, Elsevier, vol. 123(C), pages 756-766.
    3. Wu, Siyuan & Cai, Chang & Zhang, Lei & Hu, Zhiqiang & Sun, Xiangyu & Zhong, Xiaohui & Peng, Chaoyi & Meng, Keqilao & Kou, Jianyu & Li, Qing’an, 2025. "Optimizing wind turbine blade performance: A multi-objective approach for power, load and stall characteristics," Energy, Elsevier, vol. 331(C).
    4. Wang, Longyan & Chen, Meng & Luo, Zhaohui & Zhang, Bowen & Xu, Jian & Wang, Zilu & Tan, Andy C.C., 2024. "Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data," Energy, Elsevier, vol. 291(C).
    5. Sun, Haiying & Yang, Hongxing, 2018. "Study on an innovative three-dimensional wind turbine wake model," Applied Energy, Elsevier, vol. 226(C), pages 483-493.
    6. Li, Hang & Yang, Qingshan & Li, Tian, 2024. "Wind turbine wake prediction modelling based on transformer-mixed conditional generative adversarial network," Energy, Elsevier, vol. 291(C).
    7. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
    8. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    9. 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).
    10. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
    11. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    12. 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.
    13. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    14. 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).
    15. Li, Li & Wang, Bing & Ge, Mingwei & Huang, Zhi & Li, Xintao & Liu, Yongqian, 2023. "A novel superposition method for streamwise turbulence intensity of wind-turbine wakes," Energy, Elsevier, vol. 276(C).
    16. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    17. Feng, Dachuan & Gupta, Vikrant & Li, Larry K.B. & Wan, Minping, 2024. "An improved dynamic model for wind-turbine wake flow," Energy, Elsevier, vol. 290(C).
    18. Zhang, Ziyu & Huang, Peng & Bitsuamlak, Girma & Cao, Shuyang, 2024. "Large-eddy simulation of upwind-hill effects on wind-turbine wakes and power performance," Energy, Elsevier, vol. 294(C).
    19. 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).
    20. Hu, Weicheng & Liu, Zhenqing & Li, Tian & Deng, Yangchen & Guo, Wenjie, 2025. "A double Gaussian wake model for single and multiple yawed wind turbines," Energy, Elsevier, vol. 324(C).
    21. Li, Li & Huang, Zhi & Ge, Mingwei & Zhang, Qiying, 2022. "A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes," Energy, Elsevier, vol. 238(PB).
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