Decomposition Characteristics of SF6 and Partial Discharge Recognition for Inflatable DC Wall Bushing Under Negative DC Conditions

被引：0

作者：

Zhu N. ^{[1
]}

Zhang M. ^{[2
]}

Xu X. ^{[1
]}

Tang J. ^{[2
]}

Zeng F. ^{[2
]}

机构：

[1] Kunming Power Supply Bureau, Yunnan Power Grid Limited Liability Company, Kunming

[2] School of Electrical Engineering and Automation, Wuhan University, Wuhan

来源：

Zeng, Fuping (Fuping.zeng@whu.edu.cn) | 1600年 / Science Press卷 / 46期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

Back propagation neural network; Concentration ratio; Decomposed components; Partial discharge; PD recognition; SF[!sub]6[!/sub;

D O I：

10.13336/j.1003-6520.hve.20190215006

中图分类号：

学科分类号：

摘要：

To obtain the SF6 decomposition characteristics under different types of negative direct-current (DC) partial discharge, in preparation for further diagnosis and assessment of faults severity, we designed four typical internal insulation defects of DC inflatable wall-through bushings, and performed a series of experiments. The experimental results show that the 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 quantity for PD recognition, which can obtain a satisfactory effect in recognizing the PD types. © 2020, High Voltage Engineering Editorial Department of CEPRI. All right reserved.

引用

页码：947 / 956

页数：9

共 21 条

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