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Wind tunnel tests for wind turbines: A state-of-the-art review

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  • He, Ruiyang
  • Sun, Haiying
  • Gao, Xiaoxia
  • Yang, Hongxing

Abstract

Wind turbine (WT) experiments in wind tunnels can benefit the efficient utilization of wind energy in many aspects, such as the testing of new products, the validation of numerical models, and the exploration of underlying mechanisms of WT-induced flow field. However, there is a lack of comprehensive and critical review on this topic. In this paper, necessary pre-experimental works are presented first in terms of the design process of miniature WT, scaling criteria, blockage correction, and devices and methods for measurement. In addition, the wind tunnel tests on flat and complex terrains are discussed for in-depth understanding. The dynamic wake behaviors in both near wake and far wake are reviewed with the clarification of two hypotheses of wake meandering. Careful microsite selections of WTs on complex terrain can be beneficial for the total power yields. Furthermore, special attention is paid to active yaw control to verify the potential to increase power output while mitigating structural loads. Finally, discussion on research limitations and prospects are given as avenues for future research. This study provides a comprehensive overview and in-depth discussion on WT experiments in wind tunnels, which is expected to facilitate the solution of theoretical problems in wind energy and enable more practical research.

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  • He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
  • Handle: RePEc:eee:rensus:v:166:y:2022:i:c:s1364032122005664
    DOI: 10.1016/j.rser.2022.112675
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    Cited by:

    1. He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
    2. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).

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