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Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance

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  • Shin, Dongheon
  • Ko, Kyungnam

Abstract

The blade rotation of wind turbines causes the wake effect, which affects energy production of downstream wind turbines, and its magnitude and intensity vary with distance. To optimize the wind turbine layout for maximum efficiency, it is necessary to quantitatively evaluate the wake effect by distance. Using a nacelle LiDAR, it is possible to concurrently measure wind conditions at multiple positions. This study presents the results of analyzing the wake effects by distance, behind a 3 MW wind turbine, using a nacelle LiDAR. Wake wind data measured at 10 points by a nacelle LiDAR, mounted on the nacelle of a 1.5 MW downstream wind turbine, were analyzed against freestream wind data obtained from a 70-m-high met mast installed near the turbine. Variations in wind conditions in the wake region were analyzed as a function of distance and wind direction, and the influence of these factors on the power production of the downstream wind turbine was estimated using SCADA. The variations in wind speeds and turbulence intensities at multiple distances between 0.9D and 4.8D were quantitatively evaluated. Additionally, it was confirmed that the wake effects reduced the power performance by 23% at 5.7D under a wind speed of 8.5 m/s.

Suggested Citation

  • Shin, Dongheon & Ko, Kyungnam, 2022. "Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221033375
    DOI: 10.1016/j.energy.2021.123088
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    References listed on IDEAS

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    1. Fei, Zhao & Tengyuan, Wang & Xiaoxia, Gao & Haiying, Sun & Hongxing, Yang & Zhonghe, Han & Yu, Wang & Xiaoxun, Zhu, 2020. "Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm," Energy, Elsevier, vol. 199(C).
    2. Jeon, Sanghyeon & Kim, Bumsuk & Huh, Jongchul, 2015. "Comparison and verification of wake models in an onshore wind farm considering single wake condition of the 2 MW wind turbine," Energy, Elsevier, vol. 93(P2), pages 1769-1777.
    3. Dongheon Shin & Kyungnam Ko, 2019. "Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement," Energies, MDPI, vol. 12(6), pages 1-15, March.
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    Citations

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    Cited by:

    1. Zhou, Lei & Wen, Jiahao & Wang, Zhaokun & Deng, Pengru & Zhang, Hongfu, 2023. "High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM," Energy, Elsevier, vol. 275(C).
    2. 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).
    3. Hongtao Niu & Congxin Yang & Yin Wang, 2023. "Experimental Study on the Influence of Incoming Flow on Wind Turbine Power and Wake Based on Wavelet Analysis," Energies, MDPI, vol. 16(16), pages 1-15, August.
    4. Wen, Jiahao & Zhou, Lei & Zhang, Hongfu, 2023. "Mode interpretation of blade number effects on wake dynamics of small-scale horizontal axis wind turbine," Energy, Elsevier, vol. 263(PA).
    5. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).

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