Fault Diagnosis Method of Circuit Breaker Energy Storage Mechanism Based on Electro-vibration Signal Entropy Weight Feature

ZHAO Shutao; LI Yunpeng; WANG Erxu; ZHU Jipeng; MA Li; WANG Kedeng

doi:10.13296/j.1001-1609.hva.2019.11.030

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Fault Diagnosis Method of Circuit Breaker Energy Storage Mechanism Based on Electro-vibration Signal Entropy Weight Feature

更新时间：2025-04-26

- Fault Diagnosis Method of Circuit Breaker Energy Storage Mechanism Based on Electro-vibration Signal Entropy Weight Feature
- High VoltageApparatus Vol. 55, Issue 11, Pages: 204-210(2019)
- 作者机构：
  
  1. 华北电力大学电气与电子工程学院
  2. 国网保定供电公司
  3. 3. 贵州电网贵阳花溪供电局
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2019.11.030
  CLC： TM561
- Published：2019
- 稿件说明：
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[1]赵书涛,李云鹏,王二旭,朱继鹏,马莉,王科登.基于电—振信号熵权特征的断路器储能机构故障诊断方法[J].高压电器,2019,55(11):204-210.

ZHAO Shutao, LI Yunpeng, WANG Erxu, et al. Fault Diagnosis Method of Circuit Breaker Energy Storage Mechanism Based on Electro-vibration Signal Entropy Weight Feature[J]. High voltageapparatus, 2019, 55(11): 204-210.
[1]赵书涛,李云鹏,王二旭,朱继鹏,马莉,王科登.基于电—振信号熵权特征的断路器储能机构故障诊断方法[J].高压电器,2019,55(11):204-210. DOI： 10.13296/j.1001-1609.hva.2019.11.030.

ZHAO Shutao, LI Yunpeng, WANG Erxu, et al. Fault Diagnosis Method of Circuit Breaker Energy Storage Mechanism Based on Electro-vibration Signal Entropy Weight Feature[J]. High voltageapparatus, 2019, 55(11): 204-210. DOI： 10.13296/j.1001-1609.hva.2019.11.030.

摘要

弹簧势能的可靠储存是保障断路器后续分合闸操作正确的前提。通过分析控制回路、电机、传动部件和弹簧之间的能量转换和传递关系

提出一种电—振信号熵权特征联合的断路器储能机构故障类型辨识方法。首先利用希尔伯特变换（Hilbert transform）提取储能电机电流信号包络时域特征并计算峭度;然后由局部均值分解（LMD）处理断路器储能过程伴随的振动信号得到乘积函数分量（PF）

并提取PF能量谱频域的盒维数特征;进而采用熵权法对电流和振动信号待分类样本进行剪辑

按照样本信息量和重要性赋予对应的权重;最后把熵权特征作为KNN算法的输入

辨识储能机构运行状态。实验结果表明:采用联合电—振信号的熵权特征量

KNN算法能更准确地诊断断路器储能机构发生的故障类型。

Abstract

By analyzing the circuit breaker’s energy conversion and transmission relationship among control loop

motor

transmission component and spring

a fault identification method for the circuit breaker energy storage mechanism is proposed on the basis of electro-vibration signal entropy weight feature. Firstly

the Hilbert transform（HT）is used to extract the time-domain characteristics of current signal envelope of the energy storage motor. Then

the local mean decomposition（LMD）is used to process the vibration signal accompanying the energy storage process of the circuit breaker so as to obtain the product function component（PF）and extract the box dimension feature of the PF energy spectrum in frequency domain. Further

the entropy weight method is used to edit the samples of the current and vibration signals to be classified

and the corresponding weights are given according to the samples’ information quantity and importance. Finally

the entropy weight feature is used as the input of the KNN algorithm to identify the operating state of the energy storage mechanism. Experimental results show that the KNN algorithm can more accurately diagnose fault type of the circuit breaker energy storage mechanism by using the entropy weight feature of the combined electro-vibration signal.

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