ANOMALY DETECTION METHOD FOR WIND POWER BASED ON DISTRIBUTION CHARACTERISTICS

Miao Changxin; Zhou Zhiwei; Yang Qianxi; Xi Jian; Han Li

doi:10.19912/j.0254-0096.tynxb.2024-0443

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (7) : 395-402. DOI: 10.19912/j.0254-0096.tynxb.2024-0443

Special Topics of Academic Papers at the 67th Annual Meeting of the China Association for Science and Technology

ANOMALY DETECTION METHOD FOR WIND POWER BASED ON DISTRIBUTION CHARACTERISTICS

Miao Changxin¹, Zhou Zhiwei¹, Yang Qianxi¹, Xi Jian², Han Li¹

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Abstract

A large amount of abnormal samples are obtained in the operational data collected from wind farms, which prevent the implementation of tasks such as state assessment and power prediction. To overcome this issue, a recognition method which selects targeted detection methods based on different abnormal distribution characteristics in measured wind turbine operational datasets is proposed in the article. The method considers the working state of the unit and uses an adaptive clustering algorithm with noise density, taking wind speed, power, and blade pitch angle as inputs, and the minimum average distance as the objective function to achieve parameter optimization of the algorithm. In order to verify the effectiveness of the model, the power curve of the cleaned data is fitted using the least squares method, and then the absolute average error is calculated and compared with other commonly used algorithms on actual datasets in China.

Key words

anomaly detection / wind power / adaptive algorithms / cluster analysis / change point grouping

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Miao Changxin, Zhou Zhiwei, Yang Qianxi, Xi Jian, Han Li. ANOMALY DETECTION METHOD FOR WIND POWER BASED ON DISTRIBUTION CHARACTERISTICS[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 395-402 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0443

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