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

Ju Tang, Xu Yang, Dong Yang, Qiang Yao, Yulong Miao, Chaohai Zhang and Fuping Zeng
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Ju Tang: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Xu Yang: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Dong Yang: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Qiang Yao: Chongqing Electric Power Research Institute, Chongqing Power Company, Chongqing 401123, China
Yulong Miao: Chongqing Electric Power Research Institute, Chongqing Power Company, Chongqing 401123, China
Chaohai Zhang: State Grid Electric Power Research Institute, Wuhan NARI Co., Ltd., Wuhan 430072, China
Fuping Zeng: School of Electrical Engineering, Wuhan University, Wuhan 430072, China

Energies, 2017, vol. 10, issue 8, 1-17

Abstract: 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.

Keywords: SF 6; partial discharge severity; DC gas-insulated equipment; feature selection; back propagation neural network; support vector machine; decomposed component analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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