直流负电压下基于SF6分解特性的GIS局部放电类型识别

Xu Yang, Yi Liu, Yi Jiang, Hao Wen, Jing Zhang, Jia Chen
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引用次数: 0

摘要

为了利用SF6分解特性识别局部放电(PD)下直流气体绝缘开关设备(GIS)的故障,笔者选取6个实验电压代表不同的局部放电阶段,进行SF6分解实验。实验结果表明,SF6分解产物包括CF4、CO2、SO2F2、SOF2和SO2 5种稳定组分,其中SOF2是最重要的分解产物,且含硫组分浓度高于含碳组分。最后,构建由21个浓度比组成的特征集,并采用最大相关最小冗余准则进行特征选择。采用BP神经网络和支持向量机进行故障诊断,准确率高于88%。研究工作为今后基于SF6分解特性的直流GIS在线监测和绝缘状态评价奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Type Identification of GIS Partial Discharge Based on SF6 Decomposition Characteristics under Negative DC Voltage
In order to use SF6 decomposition characteristics to identify faults of DC gas-insulated switchgear (GIS) under partial discharge (PD), the author chooses 6 experimental voltages to represent different PD stages, and carries out SF6 decomposition experiments. 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 decomposed product, and the concentration of sulfur-containing components is higher than that of carbon-containing components. Finally, a feature set consisting of 21 concentration ratios is constructed, and the maximum relevance minimum redundancy criterion is used for the feature selection. BP neural network and support vector machine are used for fault diagnosis, and the accuracy rate is higher than 88%. The research work lays the foundation for the on-line monitoring and insulation state evaluation of DC GIS based on SF6 decomposition characteristics in the future.
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