基于关联信息熵和轻量级梯度提升机的油纸绝缘特征优选策略

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Abstract In order to fully explore the aging features, which are beneficial for comprehensive diagnosis results of transformer oil-paper insulation, a feature optimization strategy based on correlation information entropy and light gradient boosting machine (LightGBM) is proposed. Firstly, the initial high-dimensional feature space is formed with various time-domain features, which are extracted from the measured data of dielectric response of transformers in different aging states. Secondly, the correlation and redundancy of feature subsets is measured by correlation information entropy. Then the importance of features is evaluated according to LightGBM, so as to obtain the optimal feature space. Finally, the diagnostic performance of the optimal feature space is compared and analyzed against different control groups, and the superiority of the optimal feature space determined through the proposed optimization strategy is effectively verified.

Key words： oil-paper insulation aging comprehensive diagnosis correlation information entropy gradient boosting algorithm feature selection

Received: 23 October 2023

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	Articles by authors
	LAI Wenhong
	LIU Qingzhen
	YAN Renwu

Cite this article:

LAI Wenhong,LIU Qingzhen,YAN Renwu. Optimization strategy for oil-paper insulation features based on correlation information entropy and light gradient boosting machine[J]. Electrical Engineering, 2024, 25(1): 34-41.

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http://dqjs.cesmedia.cn/EN/Y2024/V25/I1/34

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