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

• Published in: Energies (Basel) Vol. 10; no. 4; p. 556
• Main Authors: Tang, Ju, Yang, Xu, Ye, Gaoxiang, Yao, Qiang, Miao, Yulong, Zeng, Fuping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.04.2017
• Subjects: Back propagation; Back propagation networks; back propagation neural network; Carbon dioxide; concentration ratio; decomposed components; Decomposition; Insulation; negative DC-PD; Neural networks; PD recognition; Recognition; SF6; Sulfur dioxide
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en10040556

Four typical types of artificial defects are designed in conducting the decomposition experiments of SF6 gas to obtain and understand the decomposition characteristics of SF6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition characteristics of SF6 in diagnosing the type and severity of insulation fault in DC SF6 gas-insulated equipment. Experimental results show that the negative DC partial discharges caused by the four defects decompose the SF6 gas and generate five stable decomposed components, namely, CF4, CO2, SO2F2, SOF2, and SO2. The concentration, effective formation rate, and concentration ratio of SF6 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 SF6 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.