Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions

Abstract

Four typical types of artificial defects are designed in conducting the decomposition experiments of SF 6 gas to obtain and understand the decomposition characteristics of SF 6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition characteristics of SF 6 in diagnosing the type and severity of insulation fault in DC SF 6 gas-insulated equipment. Experimental results show that the negative DC partial discharges caused by the four defects decompose the SF 6 gas and generate five stable decomposed components, namely, CF 4 , CO 2 , SO 2 F 2 , SOF 2 , and SO 2 . The concentration, effective formation rate, and concentration ratio of SF 6 decomposed components can be associated with the PD types. Furthermore, back propagation neural network algorithm is used to recognize the PD types. The recognition results show that compared with the concentrations of SF 6 decomposed components, their concentration ratios are more suitable as the characteristic quantities for PD recognition, and using those concentration ratios in recognizing the PD types can obtain a good effect.

Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

“…Many studies show that the SF 6 gas will decompose under PD and the decomposed components will further react with moisture and oxygen in the gas chamber of GIS to generate a , and SF 4 . By detecting these decomposed components in the GIS gas chamber, it is possible to determine whether there is a PD source [23,24].…”

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

“…The concentration ratios of SF 6 decomposed components for the PD recognition in GIE are proposed according to the three-ratio method used in oil chromatographic analysis to eliminate the influence of the volume effect and time effect on the fault diagnosis [22][23][24]. These concentration ratios have definite physical meaning but are not necessarily the optimal feature subset for PD recognition.…”

“…The formation process of SF6 decomposed components is shown in Figure 7 [24]. CF4 is generated through the reaction of C atom with F atom.…”

“…The back propagation neural network (BPNN) [24,[34][35][36][37][38][39][40][41] and support vector machine (SVM) [41][42][43][44][45][46][47] algorithms are used to diagnose the PD severity. The concentrations and concentration ratios of SF 6 decomposed components are selected as the input data, and three PD levels (mild PD, medium PD, and dangerous PD) are the output results.…”

“…A minimum-redundancy-maximum-relevance (mRMR)-based feature selection algorithm [28][29][30][31][32][33] is used to sort the concentrations and concentration ratios of SF6 decomposed components. Back propagation neural network (BPNN) [24,[34][35][36][37][38][39][40][41] and support vector machine (SVM) [41][42][43][44][45][46][47] algorithms are used to diagnose the PD severity. This research proposes the feature subset that can effectively characterize the PD severity and serves as a foundation for using the DCA method to diagnose the insulation faults in DC-GIE and assess its insulation status.…”