Insulation Fault Diagnosis Based on Short-term SF<sub>6</sub> Gas Decomposition Product Data

WANG Bowei; YANG Tongzhong; WANG Xianpei; ZHAO Le

doi:10.13296/j.1001-1609.hva.2018.11.007

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Insulation Fault Diagnosis Based on Short-term SF₆ Gas Decomposition Product Data

更新时间：2025-04-26

- Insulation Fault Diagnosis Based on Short-term SF₆ Gas Decomposition Product Data
- High VoltageApparatus Vol. 54, Issue 11, Pages: 55-61(2018)
- 作者机构：
  
  1. 武汉大学电气工程学院
  2. 武汉大学电子信息学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2018.11.007
  CLC： TM595
- Published：2018
- 稿件说明：
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[1]王博伟,杨同忠,王先培,赵乐.基于短期SF_6气体分解产物数据的绝缘故障诊断研究[J].高压电器,2018,54(11):55-61.

WANG Bowei, YANG Tongzhong, WANG Xianpei, et al. Insulation Fault Diagnosis Based on Short-term SF₆ Gas Decomposition Product Data[J]. High voltageapparatus, 2018, 54(11): 55-61.
[1]王博伟,杨同忠,王先培,赵乐.基于短期SF_6气体分解产物数据的绝缘故障诊断研究[J].高压电器,2018,54(11):55-61. DOI： 10.13296/j.1001-1609.hva.2018.11.007.

WANG Bowei, YANG Tongzhong, WANG Xianpei, et al. Insulation Fault Diagnosis Based on Short-term SF₆ Gas Decomposition Product Data[J]. High voltageapparatus, 2018, 54(11): 55-61. DOI： 10.13296/j.1001-1609.hva.2018.11.007.

摘要

维护电力系统中气体绝缘封闭组合开关设备的稳定运行

通过SF

气体分解组分对设备绝缘状态进行早期的故障诊断是其中关键步骤。为了提升短期SF

数据存在识别率不高技术难点

首先提出基于信息熵理论和主成分分析法的综合评价方法。然后建立数据变量特征评价模型

对于气体特征量选取有指导意义。其次通过搭建局部放电实验平台对SF

气体进行分解实验

得到的短期数据进行方法验证。最后

使用原数据与通过验证后新特征变量数据进行多分类SVM分类比对

结果表明

满足评价模型数据可以提高其识别正确率。

Abstract

To maintain stable operation of the gas-insulated closed combination switchgear in power system

early fault diagnosis of the equipment insulation state through the SF

gas decomposition components is a vital step. To im-prove the recognition rate of short-term SF

data

this study put forward a comprehensive evaluation method based onthe information entropy theory and the principal component analysis method. And a data variable feature evaluationmodel was established for selection of characteristic variables of gas. Then

decomposition experiment of SF

gas wascarried out by setting up a partial discharge experimental platform to obtain the short-term data.

Classification com-parison via multi-classification SVM between the original data and the verified new characteristic variable data shows that the recognition accuracy could be improved if the evaluation model data are satisfied.

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Insulation Fault Diagnosis Based on Short-term SF6 Gas Decomposition Product Data

DOI：10.13296/j.1001-1609.hva.2018.11.007

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Abstract

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references

Insulation Fault Diagnosis Based on Short-term SF₆ Gas Decomposition Product Data