PREDICTIVE WAKE OPTIMIZATION CONTROL FOR FLOATING WIND FARM MODELS CONSIDERING TILT AND YAW

Wang Xiaodong; Li Binbin; Liu Yingming; Zhu Ruonan; Wang Ruojin; Xu Xuefeng

doi:10.19912/j.0254-0096.tynxb.2023-1283

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (12) : 235-242. DOI: 10.19912/j.0254-0096.tynxb.2023-1283

PREDICTIVE WAKE OPTIMIZATION CONTROL FOR FLOATING WIND FARM MODELS CONSIDERING TILT AND YAW

Wang Xiaodong, Li Binbin, Liu Yingming, Zhu Ruonan, Wang Ruojin, Xu Xuefeng

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Abstract

A curled wake model for floating wind farms suitable for wake optimization control is proposed based on the characteristics of floating wind turbines. This model focuses on the dynamic characteristics of the inclined wake of units in floating wind farms, and considers the calculation of inclination and yaw deviation based on the quasi steady state curled wake model. On this basis, a model predictive wake optimization control method is proposed to coordinate the output of each unit with the goal of maximizing the output power of a floating wind farm. In FAST.Farm, the accuracy of the established models is verified by comparing the wake loss velocities calculated by the quasi steady state curled wake model （curl-N）, initial curled wake model （curl-O）, polar wake model （Polar）, and large eddy simulation （LES）. To demonstrate the effectiveness of the optimization control method, the output characteristics are compared with those of wake free optimization and traditional wake optimization. The results show that the proposed quasi steady state curled wake model can be applied to wake optimization control, and the model predicts that wake optimization control can effectively improve the overall output of floating wind farms.

Key words

offshore wind farms / wakes / model predictive control / optimize control / power generation increase

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Wang Xiaodong, Li Binbin, Liu Yingming, Zhu Ruonan, Wang Ruojin, Xu Xuefeng. PREDICTIVE WAKE OPTIMIZATION CONTROL FOR FLOATING WIND FARM MODELS CONSIDERING TILT AND YAW[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 235-242 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1283

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