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Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity

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  • Qian, Guo-Wei
  • Ishihara, Takeshi

Abstract

A new multiple wake model is developed for wind farm power prediction and wind farm control. First, numerical simulations are conducted for two wind turbines under different layout sets, and the characteristics of mean velocity and turbulence intensity in multiple wakes are systematically investigated. A new multiple wake model considering the local effective turbulence on the rotor and the wake interaction effects is proposed. The proposed model can favorably predict the mean velocity and turbulence intensity distributions in multiple wake regions, as well as the power production in wind farm comparing with numerical simulations and field measurements. Finally, the new proposed multiple wake model is applied to wind farm modelling and optimization framework, which enables the maximization of wind farm power production by wake steering control. The wind sector width of 2° with the wind speed bin of 0.5 m/s is proposed for the lookup-table-based wake steering optimization. The proposed values reduce the prediction error of annual energy production gain from 34.5% to 3.2% comparing with the conventional values of 5° and 1 m/s. In addition, the yaw offset limit of ±15° is recommended to satisfy both the maximization of power production and the safety requirement of International Electrotechnical Commission (IEC) standard.

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  • Qian, Guo-Wei & Ishihara, Takeshi, 2021. "Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity," Energy, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:energy:v:220:y:2021:i:c:s0360544220327870
    DOI: 10.1016/j.energy.2020.119680
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    7. Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lv, Tao & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu, 2023. "Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method," Energy, Elsevier, vol. 263(PD).
    8. Qian, Guo-Wei & Ishihara, Takeshi, 2022. "A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain," Energy, Elsevier, vol. 261(PA).
    9. 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).
    10. Tong Shu & Young Hoon Joo, 2023. "Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach," Energies, MDPI, vol. 16(20), pages 1-21, October.
    11. 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.
    12. Dongqin Zhang & Yang Liang & Chao Li & Yiqing Xiao & Gang Hu, 2022. "Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation," Energies, MDPI, vol. 15(19), pages 1-26, October.
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