Research on Fault Diagnosis of Circuit Breaker Based on Particle Swarm Optimization Extreme Learning Machine

ZHANG Jia; CHEN Zhiying; CHEN Li’an; CHEN Qingrong

doi:10.13296/j.1001-1609.hva.2020.06.026

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Research on Fault Diagnosis of Circuit Breaker Based on Particle Swarm Optimization Extreme Learning Machine

更新时间：2025-04-26

- Research on Fault Diagnosis of Circuit Breaker Based on Particle Swarm Optimization Extreme Learning Machine
- High VoltageApparatus Vol. 56, Issue 6, Pages: 181-188(2020)
- 作者机构：
  
  1. 厦门理工学院电气工程与自动化学院
  2. 厦门斯玛特思智能电气有限公司
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.06.026
  CLC： TM561
- Published：2020
- 稿件说明：
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[1]张佳,陈志英,陈丽安,陈庆荣.基于粒子群优化极限学习机的断路器故障诊断方法研究[J].高压电器,2020,56(06):181-188.

ZHANG Jia, CHEN Zhiying, CHEN Li’an, et al. Research on Fault Diagnosis of Circuit Breaker Based on Particle Swarm Optimization Extreme Learning Machine[J]. High voltageapparatus, 2020, 56(6): 181-188.
[1]张佳,陈志英,陈丽安,陈庆荣.基于粒子群优化极限学习机的断路器故障诊断方法研究[J].高压电器,2020,56(06):181-188. DOI： 10.13296/j.1001-1609.hva.2020.06.026.

ZHANG Jia, CHEN Zhiying, CHEN Li’an, et al. Research on Fault Diagnosis of Circuit Breaker Based on Particle Swarm Optimization Extreme Learning Machine[J]. High voltageapparatus, 2020, 56(6): 181-188. DOI： 10.13296/j.1001-1609.hva.2020.06.026.

摘要

断路器动作时

分合闸线圈电流特征能够反映断路器操动机构或者二次回路的运行状态

文中基于分合闸线圈电流特征值创建了样本库

并提出使用粒子群优化学习机进行故障诊断的方法。该方法首先通过粒子群优化算法寻找最优解

即极限学习机模型中输入层与隐含层间的权值以及隐含层的偏置

然后利用最优值进行极限学习机网络训练

最后使用训练好的网络对测试样本进行诊断并验证该方法的有效性。同时搭建了未优化的极限学习机模型和遗传算法优化的极限学习机模型

仿真结果表明

经过粒子群算法优化后的极限学习机能100%识别出样本中不同的断路器故障状态

相比另外两种算法具有更好的稳定性和更高的精确度。

Abstract

When the circuit breaker is operated

the opening and closing coil current can reflect the operating stateof the circuit breaker operating mechanism or the secondary circuit. In this paper

a sample library is created basedon the current characteristic values of the opening and closing coils

and a particle swarm optimization learning ma-chine is proposed for fault diagnosis. Firstly

the method searches for the optimal solution through the particle swarmoptimization algorithm

that is

the weight between the input layer and the hidden layer in the extreme learning ma-chine model as well as the bias of the hidden layer

then uses the optimal value to train the extreme learning machinenetwork. Finally

the trained network is used to diagnose the test samples and verify the effectiveness of the method.At the same time

the unoptimized extreme learning machine model and the genetic algorithm optimized extremelearning machine model are built. The simulation results show that the extreme learning function optimized by theparticle swarm optimization algorithm can identify 100% of the different circuit breaker fault states in the sample

which has better stability and higher accuracy than the other two algorithms.

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