Using SF 6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect

The decomposition characteristics of a SF 6 gas-insulated medium were used to diagnose the partial discharge (PD) severity in DC gas-insulated equipment (DC-GIE). First, the PD characteristics of the whole process were studied from the initial PD to the breakdown initiated by a free metal particle defect. The average discharge magnitude in a second was used to characterize the PD severity and the PD was divided into three levels: mild PD, medium PD, and dangerous PD. Second, two kinds of voltage in each of the above PD levels were selected for the decomposition experiments of SF 6 . Results show that the negative DC-PD in these six experiments decomposes the SF 6 gas and generates five stable decomposed components, namely, CF 4 , CO 2 , SO 2 F 2 , SOF 2 , and SO 2 . The concentrations and concentration ratios of the SF 6 decomposed components can be associated with the PD severity. A minimum-redundancy-maximum-relevance (mRMR)-based feature selection algorithm was used to sort the concentrations and concentration ratios of the SF 6 decomposed components. Back propagation neural network (BPNN) and support vector machine (SVM) algorithms were used to diagnose the PD severity. The use of C (CO 2 )/ CT 1 , C (CF 4 )/ C (SO 2 ), C (CO 2 )/ C (SOF 2 ), and C (CF 4 )/ C (CO 2 ) shows good performance in diagnosing PD severity. This finding serves as a foundation in using the SF 6 decomposed component analysis (DCA) method to diagnose the insulation faults in DC-GIE and assess its insulation status.