DATA-DRIVEN ADAPTIVE OPTIMIZATION OF WIND TURBINE YAW PARAMETERS

Liu Yingming; Chen Liang; Wang Xiaodong; Xie Hongfang

doi:10.19912/j.0254-0096.tynxb.2021-0022

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 366-372. DOI: 10.19912/j.0254-0096.tynxb.2021-0022

DATA-DRIVEN ADAPTIVE OPTIMIZATION OF WIND TURBINE YAW PARAMETERS

Liu Yingming¹, Chen Liang¹, Wang Xiaodong¹, Xie Hongfang²

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Abstract

The frequency and range of wind direction change under different geographical conditions are different, and the adaptive level of wind turbine yaw control is poor, which significantly affects the power generation. In order to enhance the adaptive level of wind turbine yaw system and improve the utilization of wind energy, a wind turbine yaw control parameter optimization method based on K-nearest neighbor clustering （KNN） algorithm is proposed. In order to accurately describe wind direction changes, an improved Weibull probability distribution was established to establish a wind direction evaluation model. The amplitude of wind direction fluctuations （A） and the duration of fluctuations （T） are used as data labels for wind conditions to describe the wind direction. The operating data of wind turbines under different yaw parameters are compared to determine the clustering center （the best yaw parameter under known wind conditions）. The wind turbine yaw control parameters based on the KNN algorithm are optimized. Therefore, the best yaw parameters of wind turbines under different wind conditions are obtained. Case studies show that the proposed method can improve the power generation efficiency of wind turbines at high wind speeds and reduce the number of yaw starts at low wind speeds.

Key words

wind turbines / k-nearest neighbors / Weibull distribution / yaw parameter optimization

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Liu Yingming, Chen Liang, Wang Xiaodong, Xie Hongfang. DATA-DRIVEN ADAPTIVE OPTIMIZATION OF WIND TURBINE YAW PARAMETERS[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 366-372 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0022

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