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Experimental Study on the Influence of Incoming Flow on Wind Turbine Power and Wake Based on Wavelet Analysis

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  • Hongtao Niu

    (School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    School of Chemistry and Chemical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Congxin Yang

    (School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    Gansu Provincial Technology Centre for Wind Turbines, Lanzhou 730050, China)

  • Yin Wang

    (Wind Power Division, Zhuzhou Electric Locomotive Research Institute Co., Zhuzhou 412000, China)

Abstract

Taking a wind farm in the Qinghai–Tibet Plateau as the experimental site, the ZephiR Dual Mode (ZDM) LiDAR and ground-based laser LiDAR were used to scan the incoming flow and wake of the wind turbine separately. Based on wavelet analysis, the experimental study was conducted on the influence of different incoming wind speeds on the power and wake of the wind turbine. It is found that the incoming wind speeds have a great influence on the wind turbine power, and the fluctuation frequency of the wind speed is obviously higher than that of the power, that is, the scale effects of turbulence are magnified. The rotation of the wind wheel can accelerate the collapse of the large-scale turbulent structures of the incoming flow, and large-scale vortices continue to collapse into small-scale vortices, that is, the energy cascade evolution occurs. And in the wake diffusion process, the dissipation degree of the upper blade tip vortex is greater than that of the lower blade tip vortex caused by the rotation of the wind turbine. Under the same incoming flow conditions, due to the influence of tower and ground turbulence structure, the energy level connection phenomenon of the measuring points below the hub height is stronger than that above the hub height, and it weakens with the increase of the measuring distance. That is, the energy cascade of the measuring points below the hub height at 1.5 D (D is the diameter of the wind wheel) of the wake is weaker than that at 1 D of the wake. With the increase of the measuring distance of the wake, the influx of the external flow field further aggravates the momentum exchange and energy transport between the vortex clusters, that is, the influence of the external flow field gradually increases in the wake vortex pulsation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6003-:d:1218483
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    References listed on IDEAS

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    1. 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).
    2. Vergaerde, Antoine & De Troyer, Tim & Standaert, Lieven & Kluczewska-Bordier, Joanna & Pitance, Denis & Immas, Alexandre & Silvert, Frédéric & Runacres, Mark C., 2020. "Experimental validation of the power enhancement of a pair of vertical-axis wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 181-187.
    3. 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.
    4. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    5. Ahmadi, Mohammad H.B. & Yang, Zhiyin, 2021. "On wind turbine power fluctuations induced by large-scale motions," Applied Energy, Elsevier, vol. 293(C).
    6. Gao, Xiaoxia & Chen, Yao & Xu, Shinai & Gao, Wei & Zhu, Xiaoxun & Sun, Haiying & Yang, Hongxing & Han, Zhonghe & Wang, Yu & Lu, Hao, 2022. "Comparative experimental investigation into wake characteristics of turbines in three wind farms areas with varying terrain complexity from LiDAR measurements," Applied Energy, Elsevier, vol. 307(C).
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