Combined Diagnosis of Partial Discharge Based on the Multi-dimensional Characteristic Parameters

Abstract

This paper presents a comprehensive multi-parameter diagnosis method based on multiple partial discharge signals include high-frequency current, ultrasound, ultrahigh frequency (UHF) etc. First, acquire the high-frequency current, ultrasound, UHF partial discharge data under various types of defects, and extract the characteristic values, including nine basic characteristic parameters, eight phase characteristic parameters and the like. Diagnose signals respectively, with the method based on information fusion and semi-supervised learning for high-frequency current PD data, the method based on adaptive mutation parameters of particle entropy for ultrasonic signals, the method based on IIA-ART2A neural network for UHF signals. Then integrate the diagnostic results, which is the probability of fault of various defects and matrix, of different PD diagnosis signals, and analysis with the multiple classifier based on multi-parameter fuzzy integral to get the final diagnosis.