SF6 fault decomposition feature component extraction and triangle fault diagnosis method

Abstract

How to use SF 6 decomposition feature component information to judge the form and degree of gas-insulated equipment (GIE) field discharge and overthermal faults quickly is a problem that remains unresolved. Based on the existing experimental data on SF 6 typical fault decomposition, this study considers the SF 6 decomposition mechanism under typical faults and uses the maximum correlation minimum redundancy criterion to filter out three decomposition feature components characterizing GIE typical fault attributes: SOF 2 +SO 2 , CF 4 , and SO 2 F 2 . The weight of extracted feature components is optimized by the area equivalence principle, and the triangle fault diagnosis method of the SF 6 decomposition component that is suitable for the rapid diagnosis of a GIE field is constructed. The diagnosis method is comprehensively tested using faulty data in different conditions, and the comprehensive recognition rate of lab tests reaches 96.2%. Results show that the constructed triangle fault diagnosis method of the SF 6 decomposition component can diagnose the internal fault nature of a GIE and identify the types of insulation defects that induce partial discharge faults. Moreover, the constructed method in this research is simple, effective, and suitable for field maintenance and online intelligent monitoring of GIE.