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Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production

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  • Ma, Hongliang
  • Ge, Mingwei
  • Wu, Guangxing
  • Du, Bowen
  • Liu, Yongqian

Abstract

Cooperative yaw control of wind turbines can substantially increase the total power production of wind farms, especially for the ones with aligned turbines. However, a generalized and effective method to quickly determine the turbines’ yaw angles for the wind farm control is rather limited. To this end, a systematic study was carried out on the cooperative yaw control of a single column of turbines. It is found that the optimized yaw angles are not sensitive to the turbine number. All the turbines should yaw to the identical direction for power maximization except the last turbine for which no yaw is required. The cooperative yaw control strategy can be simplified to a solution space only containing two yaw angles, i.e., the yaw angle of the first turbine and the angle for the downstream yawed turbines. Then, two algebra formulas are proposed to quickly determine the two yaw angles for the wind farm control only using three available parameters, i.e., the thrust coefficient of the rotor, the wake decay coefficient and the turbine spacing. To validate the proposed formulas, high-fidelity large-eddy simulations are performed. The results demonstrate that the proposed control strategy can increase the power production significantly for cases with strong wake effects. For one of the cases with five aligned turbines, the total power can be enhanced by 17.5%.

Suggested Citation

  • Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921010515
    DOI: 10.1016/j.apenergy.2021.117691
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    References listed on IDEAS

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    Cited by:

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    3. 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.
    4. Cai, Wei & Hu, Yang & Fang, Fang & Yao, Lujin & Liu, Jizhen, 2023. "Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines," Applied Energy, Elsevier, vol. 339(C).
    5. Yu, Yang & Wu, Shibo & Yu, Jianxing & Xu, Ya & Song, Lin & Xu, Weipeng, 2022. "A hybrid multi-criteria decision-making framework for offshore wind turbine selection: A case study in China," Applied Energy, Elsevier, vol. 328(C).
    6. Shu, Tong & Song, Dongran & Joo, Young Hoon, 2022. "Non-centralised coordinated optimisation for maximising offshore wind farm power via a sparse communication architecture," Applied Energy, Elsevier, vol. 324(C).
    7. Mou Lin & Fernando Porté-Agel, 2023. "Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control," Energies, MDPI, vol. 16(6), pages 1-17, March.
    8. Yang, Haoze & Ge, Mingwei & Gu, Bo & Du, Bowen & Liu, Yongqian, 2022. "The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine," Energy, Elsevier, vol. 244(PB).
    9. Zhang, Shuaibin & Du, Bowen & Ge, Mingwei & Zuo, Yingtao, 2022. "Study on the operation of small rooftop wind turbines and its effect on the wind environment in blocks," Renewable Energy, Elsevier, vol. 183(C), pages 708-718.
    10. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).

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