IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES

Wang Zhiming; Chen Xiaoguo; Wang Lingjun; Lu Wenbin

doi:10.19912/j.0254-0096.tynxb.2023-1973

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (3) : 687-696. DOI: 10.19912/j.0254-0096.tynxb.2023-1973

IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES

Wang Zhiming, Chen Xiaoguo, Wang Lingjun, Lu Wenbin

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Abstract

According to the distribution characteristics and causes of abnormal wind power data, an improved Bayesian change point-Thompson tau abnormal data cleaning method is proposed. First, the non-zero power value less than the cut-in wind speed is eliminated. Then, abnormal values accumulated at the bottom of the power curve and the surrounding discrete outliers are removed based on the improved Bayesian change point-Thompson tau method, and the cleaned normal data are obtained. Finally, the proposed method is applied to clean the actual power wind speed data of six units in a wind field, and the evaluation indexes such as cleaning time, data deletion rate, root mean square error and determination coefficient are used to verify the proposed method. The results show that compared with quartile k-means clustering algorithm, optimal intra-group variance algorithm, Bayesian change point-quartile method and Thompson tau-quartile method, the proposed method can effectively identify and eliminate all kinds of power and wind speed outliers, and the constructed power curve has high accuracy. The cleaning time is short, cleaning effect is good and versatility is strong.

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wind power / wind turbines / data processing / power curve modeling / improved Bayesian change point-Thompson tau

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Wang Zhiming, Chen Xiaoguo, Wang Lingjun, Lu Wenbin. IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 687-696 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1973

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