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Statistical characteristics of interacting wind turbine wakes from a 7-month LiDAR measurement campaign

Author

Listed:
  • Torres Garcia, E.
  • Aubrun, S.
  • Coupiac, O.
  • Girard, N.
  • Boquet, M.

Abstract

The present study focuses on the wakes of two wind turbines that, depending on the wind direction, experienced different degrees of interactions, by processing field wake observations made from a 7-month ground based scanning LiDAR measurement campaign. This duration ensures an acceptable statistical convergence of the ensemble-averaged flow fields obtained after a classification according to the wind speed at hub height and the wind direction, and limited to neutral atmospheric stability. The mean flow fields showed a well-defined wake evolution for all configurations. As expected, the wake centerlines are aligned with the wind direction, however when wakes are in intermediate interaction, wakes centerlines are skewed. This has been imputed to the wake center determination method, which is not appropriate to dissociate multiple wakes. For the lower degree of interactions, the mean wakes are aligned. However the standard deviation of the instantaneous wake centerlines shows the mutual influence that one wake has on the other. Obtained results showed an increase in the turbulence intensity within the wake, but an asymmetric distribution was observed. Listing the possible reasons, no plausible explanation has been found. The wake meandering had been quantified by the standard deviation of the instantaneous wake centerlines, showing that this phenomenon is amplified by the level of interactions. The velocity deficit recovery showed a good agreement with proposed models, as soon as there is no neighboring wakes. If that is the case, the velocity deficit is increased in function of the position of the neighboring rotor.

Suggested Citation

  • Torres Garcia, E. & Aubrun, S. & Coupiac, O. & Girard, N. & Boquet, M., 2019. "Statistical characteristics of interacting wind turbine wakes from a 7-month LiDAR measurement campaign," Renewable Energy, Elsevier, vol. 130(C), pages 1-11.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:1-11
    DOI: 10.1016/j.renene.2018.06.030
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    References listed on IDEAS

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    1. 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.
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    Cited by:

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    2. Xu Ning & Decheng Wan, 2019. "LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics," Sustainability, MDPI, vol. 11(24), pages 1-26, December.
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    5. Hegazy, Amr & Blondel, Frédéric & Cathelain, Marie & Aubrun, Sandrine, 2022. "LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models," Renewable Energy, Elsevier, vol. 181(C), pages 457-471.
    6. Xu, Zongyuan & Gao, Xiaoxia & Zhu, Yuting & Gong, Xiaoyu & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu & Zhao, Wensheng, 2025. "Wake characteristics of wind turbine in anisotropic terrain based on field experiment combined with LES," Renewable Energy, Elsevier, vol. 252(C).
    7. Fei Zhao & Yihan Gao & Tengyuan Wang & Jinsha Yuan & Xiaoxia Gao, 2020. "Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    8. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    9. Tang, Shengming & Guo, Yun & Wang, Xu & Zhu, Rong & Tang, Jie & Zhang, Shuai, 2023. "Evaluation and impact factors of Doppler wind lidar during Super Typhoon Lekima (2019)," Renewable Energy, Elsevier, vol. 205(C), pages 305-316.

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