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Experimental investigation and analytical modelling of active yaw control for wind farm power optimization

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  • Zong, Haohua
  • Porté-Agel, Fernando

Abstract

In this study, the physics and effectiveness of active yaw control under various wind conditions are investigated systematically, based on wind tunnel experiments and a new analytical wind farm model. The power and wake velocity measurements of a three-row miniature wind farm reveal that the peak power gain (18%) is reached in partial-wake conditions, when the wind direction is misaligned with the turbine column by 2–4°. In contrast, the power gain in the full-wake condition (5.4%) is a local minimum. For a single-column wind farm, the optimal yaw angle distribution always exhibits a decreasing trend from upstream to downstream, which can be associated with the secondary wake steering effect. Analytical model predicts that with increasing number of rows, both the peak power gain and the leading-turbine yaw angle increase asymptotically. The maximum value of the yaw angle is mainly determined by the cosine exponent of the thrust coefficient (p). With a typical value of p=1.8, the maximum yaw angle value is approximately 30°. Turbulence intensity and streamwise spacing have similar effects on active yaw control. When these two parameters increase, the relative power gain decreases monotonically.

Suggested Citation

  • Zong, Haohua & Porté-Agel, Fernando, 2021. "Experimental investigation and analytical modelling of active yaw control for wind farm power optimization," Renewable Energy, Elsevier, vol. 170(C), pages 1228-1244.
    • Handle: RePEc:eee:renene:v:170:y:2021:i:c:p:1228-1244
    DOI: 10.1016/j.renene.2021.02.059
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    Citations

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    Cited by:

    1. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    2. Zhiwen Deng & Chang Xu & Zhihong Huo & Xingxing Han & Feifei Xue, 2023. "Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model," Energies, MDPI, vol. 16(9), pages 1-20, May.
    3. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
    4. He, Ruiyang & Yang, Hongxing & Lu, Lin & Gao, Xiaoxia, 2024. "Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms," Renewable Energy, Elsevier, vol. 225(C).
    5. Wei Li & Shinai Xu & Baiyun Qian & Xiaoxia Gao & Xiaoxun Zhu & Zeqi Shi & Wei Liu & Qiaoliang Hu, 2022. "Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    6. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    7. Aju, Emmanuvel Joseph & Kumar, Devesh & Leffingwell, Melissa & Rotea, Mario A. & Jin, Yaqing, 2023. "The influence of yaw misalignment on turbine power output fluctuations and unsteady aerodynamic loads within wind farms," Renewable Energy, Elsevier, vol. 215(C).
    8. 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).
    9. Can Zhang & Jisheng Zhang & Athanasios Angeloudis & Yudi Zhou & Stephan C. Kramer & Matthew D. Piggott, 2023. "Physical Modelling of Tidal Stream Turbine Wake Structures under Yaw Conditions," Energies, MDPI, vol. 16(4), pages 1-21, February.
    10. Chen, Kaixuan & Lin, Jin & Qiu, Yiwei & Liu, Feng & Song, Yonghua, 2022. "Joint optimization of wind farm layout considering optimal control," Renewable Energy, Elsevier, vol. 182(C), pages 787-796.
    11. Wei Yang & Meng Yu & Bowen Yan & Guoqing Huang & Qingshan Yang & Senqin Zhang & Tianhao Hong & Xu Zhou & Xiaowei Deng, 2022. "Wind Tunnel Tests of Wake Characteristics for a Scaled Wind Turbine Model Based on Dynamic Similarity," Energies, MDPI, vol. 15(17), pages 1-17, August.
    12. 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).

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