Using SF6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect

Abstract

The decomposition characteristics of a SF6 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 SF6. Results show that the negative DC-PD in these six experiments decomposes the SF6 gas and generates five stable decomposed components, namely, CF4, CO2, SO2F2, SOF2, and SO2. The concentrations and concentration ratios of the SF6 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 SF6 decomposed components. Back propagation neural network (BPNN) and support vector machine (SVM) algorithms were used to diagnose the PD severity. The use of C(CO2)/CT1, C(CF4)/C(SO2), C(CO2)/C(SOF2), and C(CF4)/C(CO2) shows good performance in diagnosing PD severity. This finding serves as a foundation in using the SF6 decomposed component analysis (DCA) method to diagnose the insulation faults in DC-GIE and assess its insulation status.

Keywords

• SF6
• partial discharge severity
• DC gas-insulated equipment
• feature selection
• back propagation neural network
• support vector machine
• decomposed component analysis