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Operational-dependent wind turbine wake impact on surface momentum flux

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  • Abraham, Aliza
  • Hong, Jiarong

Abstract

As wind energy continues to expand, increased interaction between wind farms and their surroundings can be expected. Using natural snowfall to visualize the air flow in the wake of a utility-scale wind turbine at unprecedented spatio-temporal resolution, intermittent periods of strong interaction between the wake and the ground surface are observed and the momentum flux during these periods is quantified. Significantly, two turbine operational-dependent pathways that lead to these periods of increased wake-ground interaction are identified. The first is caused by changes in tip speed ratio that lead to blade tip vortex leapfrogging, and the second results from increased power generation and the corresponding increase in tip vortex strength and wake expansion. Data from a nearby meteorological tower provides further insights into the strength and persistence of the enhanced flux for each pathway under different atmospheric conditions. Through the discovery of these pathways, discrepancies can be resolved between previous conflicting studies on the impact of wind turbines on surface fluxes. Furthermore, the results are used to generate a map of the potential impact of wind farms on surface momentum flux throughout the Continental United States, providing a valuable resource for wind farm siting decisions.

Suggested Citation

  • Abraham, Aliza & Hong, Jiarong, 2021. "Operational-dependent wind turbine wake impact on surface momentum flux," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  • Handle: RePEc:eee:rensus:v:144:y:2021:i:c:s1364032121003117
    DOI: 10.1016/j.rser.2021.111021
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    References listed on IDEAS

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    1. Liming Zhou & Yuhong Tian & Somnath Baidya Roy & Chris Thorncroft & Lance F. Bosart & Yuanlong Hu, 2012. "Impacts of wind farms on land surface temperature," Nature Climate Change, Nature, vol. 2(7), pages 539-543, July.
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    Cited by:

    1. Xing, Zuoxia & Chen, Mingyang & Cui, Jia & Chen, Zhe & Xu, Jian, 2022. "Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using Convolutional Neural Network," Renewable Energy, Elsevier, vol. 197(C), pages 1020-1033.
    2. Zheng, Yidan & Liu, Huiwen & Chamorro, Leonardo P. & Zhao, Zhenzhou & Li, Ye & Zheng, Yuan & Tang, Kexin, 2023. "Impact of turbulence level on intermittent-like events in the wake of a model wind turbine," Renewable Energy, Elsevier, vol. 203(C), pages 45-55.

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