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Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel

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  • Dou, Bingzheng
  • Guala, Michele
  • Lei, Liping
  • Zeng, Pan

Abstract

The wake of upstream wind turbines is known to affect the operation of downstream turbines and the efficiency of the wind farm. In this study, a systematic experimentation on performance and wake spatial evolution was carried out using a wind turbine model varying tip speed ratio, pitch and yaw angles. The change of pitch angle was observed to induce a greater effect on the wake velocity as compared to the tip speed ratio. This is interpreted in terms of “force viewpoint”, which describes more quantitatively the relationship between the turbine performance and the wake, as compared to the “power viewpoint”, based on the sole energy conversion. The turbine yaw angle is observed to cause not only a decrease in power and thrust, but also an offset and an asymmetry in the wake. The offset, quantified using the spatial distribution of the velocity minima, is modeled analytically. Comparisons of model estimations with the experimental measurements show that the proposed model can acceptably predict the wake offset of a yawed turbine. The observed dependencies of the mean velocity deficit and wake turbulence on power, thrust, and yaw angle, may suggest new derating strategies for wind farm optimization.

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  • Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel," Energy, Elsevier, vol. 166(C), pages 819-833.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:819-833
    DOI: 10.1016/j.energy.2018.10.103
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    2. Meng, Haoran & Ma, Zhe & Dou, Bingzheng & Zeng, Pan & Lei, Liping, 2020. "Investigation on the performance of a novel forward-folding rotor used in a downwind horizontal-axis turbine," Energy, Elsevier, vol. 190(C).
    3. 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.
    4. Yu-Ting Wu & Chang-Yu Lin & Che-Ming Hsu, 2020. "An Experimental Investigation of Wake Characteristics and Power Generation Efficiency of a Small Wind Turbine under Different Tip Speed Ratios," Energies, MDPI, vol. 13(8), pages 1-19, April.
    5. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    6. Salim Abdullah Bazher & Juyeol Park & Jungkeun Oh & Daewon Seo, 2024. "Numerical Investigation of Wake Characteristics for Scaled 20 kW Wind Turbine Models with Various Size Factors," Energies, MDPI, vol. 17(17), pages 1-27, September.
    7. Jiyong Lee & Mirko Musa & Chris Feist & Jinjin Gao & Lian Shen & Michele Guala, 2019. "Wake Characteristics and Power Performance of a Drag-Driven in-Bank Vertical Axis Hydrokinetic Turbine," Energies, MDPI, vol. 12(19), pages 1-20, September.
    8. Meng, Haoran & Su, Hao & Guo, Jia & Qu, Timing & Lei, Liping, 2022. "Experimental investigation on the power and thrust characteristics of a wind turbine model subjected to surge and sway motions," Renewable Energy, Elsevier, vol. 181(C), pages 1325-1337.
    9. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Lidar assisted wake redirection in wind farms: A data driven approach," Renewable Energy, Elsevier, vol. 152(C), pages 484-493.
    10. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    11. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    12. Pallotta, A. & Pietrogiacomi, D. & Romano, G.P., 2020. "HYBRI – A combined Savonius-Darrieus wind turbine: Performances and flow fields," Energy, Elsevier, vol. 191(C).
    13. Bingzheng Dou & Zhanpei Yang & Michele Guala & Timing Qu & Liping Lei & Pan Zeng, 2020. "Comparison of Different Driving Modes for the Wind Turbine Wake in Wind Tunnels," Energies, MDPI, vol. 13(8), pages 1-17, April.
    14. Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).
    15. 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).
    16. 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).
    17. Martin Geibel & Galih Bangga, 2022. "Data Reduction and Reconstruction of Wind Turbine Wake Employing Data Driven Approaches," Energies, MDPI, vol. 15(10), pages 1-40, May.
    18. Zhe Ma & Liping Lei & Earl Dowell & Pan Zeng, 2020. "An Experimental Study on the Actuator Line Method with Anisotropic Regularization Kernel," Energies, MDPI, vol. 13(4), pages 1-19, February.

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