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Derivation and verification of three-dimensional wake model of multiple wind turbines based on super-Gaussian function

Author

Listed:
  • Zhang, Shaohai
  • Gao, Xiaoxia
  • Ma, Wanli
  • Lu, Hongkun
  • Lv, Tao
  • Xu, Shinai
  • Zhu, Xiaoxun
  • Sun, Haiying
  • Wang, Yu

Abstract

Wind power production is strongly affected by wake, and wake analysis is particularly important for wind farm layout optimization. The purpose of this study is to establish a multi-dimensional analysis wake model (3DJSG-M model) which can describe the wake region of multiple wind turbines perfectly to analyze the three-dimensional spatial distribution characteristics of the wake of multiple wind turbines. Firstly, considering the wind speed difference between wind shear inflow and uniform inflow, a three-dimensional wake model based on super-Gaussian function (3DJSG model) is derived by using the flow conservation theorem; Secondly, the influence of wind shear is introduced into the energy deficit theory, and the 3DJSG-M model is derived; Finally, wind field experiments are carried out with two high-precision LIDARs to verify the accuracy of 3DJSG model and 3DJSG-M model respectively. The results show that 3DJSG model and 3DJSG-M model fit well with the experimental data and can better predict the three-dimensional distribution characteristics of the whole wake region after a single wind turbine and multiple wind turbines. This study can provide optimization strategies for wind farm layout, reduce energy costs, and improve wind farm benefits.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008741
    DOI: 10.1016/j.renene.2023.118968
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    References listed on IDEAS

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    1. Xiaoxia, Gao & Luqing, Li & Shaohai, Zhang & Xiaoxun, Zhu & Haiying, Sun & Hongxing, Yang & Yu, Wang & Hao, Lu, 2022. "LiDAR-based observation and derivation of large-scale wind turbine's wake expansion model downstream of a hill," Energy, Elsevier, vol. 259(C).
    2. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
    3. Gao, Xiaoxia & Li, Bingbing & Wang, Tengyuan & Sun, Haiying & Yang, Hongxing & Li, Yonghua & Wang, Yu & Zhao, Fei, 2020. "Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements," Applied Energy, Elsevier, vol. 260(C).
    4. Li, Qing'an & Murata, Junsuke & Endo, Masayuki & Maeda, Takao & Kamada, Yasunari, 2016. "Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (part II: Wake characteristics)," Energy, Elsevier, vol. 113(C), pages 1304-1315.
    5. Brogna, Roberto & Feng, Ju & Sørensen, Jens Nørkær & Shen, Wen Zhong & Porté-Agel, Fernando, 2020. "A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain," Applied Energy, Elsevier, vol. 259(C).
    6. 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.
    7. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    8. Cheng, Yu & Zhang, Mingming & Zhang, Ziliang & Xu, Jianzhong, 2019. "A new analytical model for wind turbine wakes based on Monin-Obukhov similarity theory," Applied Energy, Elsevier, vol. 239(C), pages 96-106.
    9. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    10. González-Longatt, F. & Wall, P. & Terzija, V., 2012. "Wake effect in wind farm performance: Steady-state and dynamic behavior," Renewable Energy, Elsevier, vol. 39(1), pages 329-338.
    11. De Cillis, Giovanni & Cherubini, Stefania & Semeraro, Onofrio & Leonardi, Stefano & De Palma, Pietro, 2022. "Stability and optimal forcing analysis of a wind turbine wake: Comparison with POD," Renewable Energy, Elsevier, vol. 181(C), pages 765-785.
    12. Keane, Aidan, 2021. "Advancement of an analytical double-Gaussian full wind turbine wake model," Renewable Energy, Elsevier, vol. 171(C), pages 687-708.
    13. Sun, Haiying & Yang, Hongxing, 2018. "Study on an innovative three-dimensional wind turbine wake model," Applied Energy, Elsevier, vol. 226(C), pages 483-493.
    14. 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).
    15. Castillo, Ricardo & Pol, Suhas, 2022. "Wind tunnel studies of wind turbine yaw and speed control effects on the wake trajectory and thrust stabilization," Renewable Energy, Elsevier, vol. 189(C), pages 726-733.
    16. 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).
    17. 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.
    18. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
    19. Husien, Walid & El-Osta, Wedad & Dekam, Elhadi, 2013. "Effect of the wake behind wind rotor on optimum energy output of wind farms," Renewable Energy, Elsevier, vol. 49(C), pages 128-132.
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