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期刊号: CN32-1800/TM| ISSN1007-3175

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基于SF6分解组分的负极性直流局部放电故障诊断

来源:电工电气发布时间:2020-09-18 16:18 浏览次数:54
基于SF6分解组分的负极性直流局部放电故障诊断
 
杨旭1,2,3,黄勤清1,2,张晓星3,文豪1,2,聂德鑫1,2,周文1,2,江翼1,2
(1 南瑞集团(国网电力科学研究院)有限公司,江苏 南京 211006;2 国网电力科学研究院武汉南瑞有限责任公司,湖北 武汉 430074;
3 武汉大学 电气与自动化学院,湖北 武汉 430072)
 
    摘 要:为了利用SF6局部放电(PD)分解特性开展直流气体绝缘设备(GIE)故障诊断研究,以直流GIE中最为常见的4 种绝缘缺陷为例,研究了缺陷从起始放电发展至临近击穿整个过程的PD特性,选择q vn v 和Δt v作为表征PD状态的特征量,并将PD严重程度划分为3 个等级;在每种缺陷的3 个PD等级下开展了大量SF6分解实验,获取了SF6分解特性。实验结果表明,SF6分解生成了CF4、CO2、SO2F2、SOF2 和SO2 5 种稳定组分,其中SOF2是最主要的分解产物,且含硫组分的生成量高于含碳组分的生成量;构建了由21 个浓度比值组成的特征集合,运用最大相关最小冗余准则进行特征量选择,并基于BP神经网络和支持向量机进行了故障诊断,准确率超过88%。
    关键词:局部放电;SF6 分解特性;最大相关最小冗余;故障诊断
    中图分类号:TM21     文献标识码:A     文章编号:1007-3175(2020)09-0001-07
 
Type Identification of Negative DC Partial Discharge Based on SF6 Decomposed Components
 
YANG Xu1, 2, 3, HUANG Qin-qing1, 2, ZHANG Xiao-xing3, WEN Hao1, 2, NIE De-xin1, 2, ZHOU Wen1, 2, JIANG Yi1, 2
(1 Nanjing NARI Group Corp(State Grid Electric Power Research Institute), Nanjing 211 006, China;
2 Wuhan NARI Co., Ltd, State Grid Electric Power Research Institute, Wuhan 430074,China;
3 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072,China)
 
    Abstract: In order to use SF6 decomposition characteristics to identify faults of DC gas-insulated equipment (GIE) under partial discharge (PD), this paper studied the PD characteristics of the whole process from the initial discharge to near breakdown of the four most common insulation defects in DC-GIE. qv, nv, and Δtv are selected as the feature quantities for characterizing the PD state, and the PD severity is divided into three levels. Then, a large number of SF6 decomposition experiments were carried out under the three PD level of each defect, and the decomposition characteristics of SF6 were obtained. The experimental results show that SF6 decomposition produces include five stable components of CF4, CO2, SO2F2, SOF2 and SO2, among which SOF2 is the most important decomposition product, and the formation amount of sulfur components are higher than that of carbonaceous components. Finally, a feature set consisting of 21 concentration ratios is constructed, and the maximum relevance minimum redundancy criterion is used for feature quantity selection. BP neural network and support vector were used for fault identification, and the accuracy rate was higher than 88%.
    Key words: partial discharge; SF6 decomposition characteristics; maximum relevance minimum redundancy; fault identification
 
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