Diagnosis of GIS Discharge Fault Based on MEEMD Approximate Entropy

ZANG Xu; MA Hongzhong; WU Jinli; LI Chengying; CHEN Ming; ZHANG Li; QU Bin

doi:10.13296/j.1001-1609.hva.2020.06.019

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Diagnosis of GIS Discharge Fault Based on MEEMD Approximate Entropy

更新时间：2025-04-26

- Diagnosis of GIS Discharge Fault Based on MEEMD Approximate Entropy
- High VoltageApparatus Vol. 56, Issue 6, Pages: 129-137(2020)
- 作者机构：
  
  1. 河海大学能源与电气学院
  2. 国网天津市电力公司电力科学研究院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.06.019
  CLC： TM855
- Published：2020
- 稿件说明：
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[1]臧旭,马宏忠,吴金利,李呈营,陈明,张利,屈斌.基于改进集总经验模态近似熵的GIS放电故障诊断[J].高压电器,2020,56(06):129-137.

ZANG Xu, MA Hongzhong, WU Jinli, et al. Diagnosis of GIS Discharge Fault Based on MEEMD Approximate Entropy[J]. High voltageapparatus, 2020, 56(6): 129-137.
[1]臧旭,马宏忠,吴金利,李呈营,陈明,张利,屈斌.基于改进集总经验模态近似熵的GIS放电故障诊断[J].高压电器,2020,56(06):129-137. DOI： 10.13296/j.1001-1609.hva.2020.06.019.

ZANG Xu, MA Hongzhong, WU Jinli, et al. Diagnosis of GIS Discharge Fault Based on MEEMD Approximate Entropy[J]. High voltageapparatus, 2020, 56(6): 129-137. DOI： 10.13296/j.1001-1609.hva.2020.06.019.

摘要

气体绝缘组合电器（GIS）作为重要输电设备

由局部放电导致GIS绝缘劣化是常见的故障

需对其进行诊断。从不同放电故障下GIS振动信号的特点出发进行GIS放电故障的识别

搭建GIS典型放电故障实验模拟平台

采集GIS正常运行与尖刺放电、悬浮放电和沿面放电3种典型放电故障的振动信号

采用改进集总经验模态分解法（MEEMD）提取不同运行状态下振动信号模态函数

计算模态函数的近似熵值

将差异较为明显的近似熵值作为特征量输入相关向量机（RVM）进行训练

建立二叉树放电模型

对测试样本进行分类

验证此方法的正确性

实现放电故障诊断。结果表明

改进集总经验模态分解可以较好提取信号中不同的频率成分

近似熵作为GIS振动信号模态函数的特征量能有效表征放电故障下信号的特点

实现对GIS放电故障进行诊断。

Abstract

As an important transmission equipment

GIS insulation degradation caused by partial discharge is a com-mon fault needing to be diagnosed. GIS discharge faults are identified based on the characteristics of GIS vibrationsignals under different discharge faults. An experimental simulation platform for its typical discharge faults of GIS isbuilt to collecting vibration signal simples under four states: the normal state

the spike discharge

suspended dis-charge and surface discharge. The method of modified ensemble empirical mode decomposition（MEEMD）is pro-posed to extracting the modal features in various states

and then calculated the approximate entropy of each modalstate

input the approximate entropy values which have significant difference to relevance vector machine（RVM）fortraining and established different discharge fault models to classify the test samples. The results show that the approx-imate entropy extracted from the MEEMD can effectively show the characteristics of the signal under discharge faultsalso can be used as GIS vibration signal features to diagnose the discharge faults of GIS effectively.

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