Aging State Evaluation of Oil-Paper Insulation Based on Fusion of Gray Texture and Oil Gas Features

doi:10.11930/j.issn.1004-9649.202004095

Abstract

Abstract: Aiming at the problem of insufficient precision of aging information obtained by single partial discharge(PD) feature discrimination, a D-S evidence fusion recognition method based on PD gray texture and oil-gas features is proposed to analyze the aging state of oil-paper insulation. Firstly, an air gap model of artificial oil-paper insulation is constructed through experiments to simulate the actual operation environment inside the transformer. Next, the PD signals of seven aging levels of oil-paper insulation and the corresponding gas content in oil are collected. Then, the gray texture features, statistical features and oil-gas features of each aging level are extracted, and subsequently identified using the support vector machine. The identification results of gray texture features and oil-gas features are then input into the D-S evidence fusion framework for further recognition and analysis. Finally, the recognition results are compared with the single feature and other two-feature fusion results. The experimental results show that the D-S evidence fusion method based on gray texture feature and oil-gas feature can better identify the aging state of oil-paper insulation.

Key words: partial discharge, gray texture feature, oil-gas feature, D-S evidence fusion

LIU Dongchao, LIN Yu, YUAN Hui, WANG Shuai, JIANG Min, YU Hua, LI Haitao. Aging State Evaluation of Oil-Paper Insulation Based on Fusion of Gray Texture and Oil Gas Features[J]. Electric Power, 2020, 53(12): 159-166,197.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202004095

https://www.electricpower.com.cn/EN/Y2020/V53/I12/159

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