Fault Recognition for High Voltage Circuit Breaker Based on EMD of Vibration Signal and Energy Entropy Characteristic

CHEN Wei-gen1; DENG Bang-fei1; YANG-Bin2

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Fault Recognition for High Voltage Circuit Breaker Based on EMD of Vibration Signal and Energy Entropy Characteristic

更新时间：2025-04-26

- Fault Recognition for High Voltage Circuit Breaker Based on EMD of Vibration Signal and Energy Entropy Characteristic
- High VoltageApparatus Vol. 45, Issue 2, Pages: 90-93+96(2009)
- 作者机构：
  
  1. 重庆大学输配电装备及系统安全与新技术国家重点实验室
  2. 重庆市电力公司杨家坪供电局
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM561
- Published：2009
- 稿件说明：
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[1]陈伟根,邓帮飞,杨彬.基于振动信号经验模态分解及能量熵的高压断路器故障识别[J].高压电器,2009,45(02):90-93+96.

CHEN Wei-gen1, DENG Bang-fei1, YANG-Bin2. Fault Recognition for High Voltage Circuit Breaker Based on EMD of Vibration Signal and Energy Entropy Characteristic[J]. High voltageapparatus, 2009, 45(2): 90-93+96.
[1]陈伟根,邓帮飞,杨彬.基于振动信号经验模态分解及能量熵的高压断路器故障识别[J].高压电器,2009,45(02):90-93+96. DOI：

CHEN Wei-gen1, DENG Bang-fei1, YANG-Bin2. Fault Recognition for High Voltage Circuit Breaker Based on EMD of Vibration Signal and Energy Entropy Characteristic[J]. High voltageapparatus, 2009, 45(2): 90-93+96. DOI：

摘要

为了准确地检测出高压断路器的故障类型

笔者首次将经验模态分解（EMD）方法引入高压断路器的振动信号分析当中

并提出将EMD分解得到的固有模态函数（IMF）能量熵值作为表征断路器故障类型的新特征向量。为了证实该分析方法的有效性

笔者在实验室的110 kV SF6断路器上进行了模拟实验

提取了正常和故障状态下振动信号的IMF能量熵值特征向量

并以此作为径向基神经网络的输入向量。最后

引入置信度的概念

对径向基神经网络的输出结果进行评价。该方法基于实验室研究取得了较好的识别效果

并为基于振动信号的断路器故障识别提供了一条新的思路。

Abstract

The Empirical Mode Decomposition（EMD） is introduced in this paper to analyze vibration signal of circuit breaker.The energy entropy of Intrinsic Mode Function（IMF） decomposed by EMD is proposed as the new characteristic feature to identify the fault type of circuit breaker.In order to verify the validity of this method

the simulation experiment is carried out on an actual 110 kV circuit breaker in our laboratory.The IMF energy entropy of vibration signal in normal and fault circuit breaker is extracted respectively and is taken as the input of radial basis function neural networks to identify the fault style.The confidence degree is also introduced in this paper to evaluate the output of neural networks.A satisfied result is obtained based on the experimental data

and the proposed method provide a novel way to the fault diagnosis of circuit breaker based on vibration signal.

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