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Correlation Characteristics Comparison of SF6 Decomposition versus Gas Pressure under Negative DC Partial Discharge Initiated by Two Typical Defects
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Energies 2017, 10(8), 1119; doi:10.3390/en10081119

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

1
School of Electrical Engineering, Wuhan University, Wuhan 430072, China
2
Chongqing Electric Power Research Institute, Chongqing Power Company, Chongqing 401123, China
3
State Grid Electric Power Research Institute, Wuhan NARI Co., Ltd., Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Received: 20 June 2017 / Revised: 27 July 2017 / Accepted: 28 July 2017 / Published: 1 August 2017
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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. View Full-Text
Keywords: SF6; partial discharge severity; DC gas-insulated equipment; feature selection; back propagation neural network; support vector machine; decomposed component analysis SF6; partial discharge severity; DC gas-insulated equipment; feature selection; back propagation neural network; support vector machine; decomposed component analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tang, J.; Yang, X.; Yang, D.; Yao, Q.; Miao, Y.; Zhang, C.; Zeng, F. Using SF6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect. Energies 2017, 10, 1119.

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