DATA-DRIVEN WIND POWER SYSTEM PERFORMANCE IMPROVEMENT AND PRACTICE

Zhu Youjun; Yan Lipeng; Zhang Chengyi; Liu Chuanliang; Wang Xiaodong

doi:10.19912/j.0254-0096.tynxb.2024-1060

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 774-780. DOI: 10.19912/j.0254-0096.tynxb.2024-1060

DATA-DRIVEN WIND POWER SYSTEM PERFORMANCE IMPROVEMENT AND PRACTICE

Zhu Youjun¹, Yan Lipeng¹, Zhang Chengyi¹, Liu Chuanliang¹, Wang Xiaodong²

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Abstract

This article proposes the analysis and mining of SCADA operation data to improve the output performance of wind turbines. By analyzing the operating status and abnormal data types of wind turbines, an improved DBSCAN clustering method is adopted to clean and detect abnormal data caused by environmental changes. By using an improved moving least squares B-spline regression model, the control characteristic parameters corresponding to the maximum unit output at different wind speeds are identified and fed back to the unit control system. On the basis of unit simulation verification, validation and analysis were carried out under actual operating conditions of real units. Actual tests show that this method significantly improved system efficiency at rated wind speed.

Key words

wind turbines / operation data / data mining / DBSCAN algorithm / moving least square method / control characteristic parameters

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Zhu Youjun, Yan Lipeng, Zhang Chengyi, Liu Chuanliang, Wang Xiaodong. DATA-DRIVEN WIND POWER SYSTEM PERFORMANCE IMPROVEMENT AND PRACTICE[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 774-780 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1060

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