Mechanical Fault Diagnosis for Spring Mechanism of High-voltage Vacuum Circuit Breaker Based on PSO-LSSVM

JIANG Hailong; LI Tongqing; CHENG Hao; HOU Chunguang

doi:10.13296/j.1001-1609.hva.2019.12.037

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Mechanical Fault Diagnosis for Spring Mechanism of High-voltage Vacuum Circuit Breaker Based on PSO-LSSVM

更新时间：2025-04-26

- Mechanical Fault Diagnosis for Spring Mechanism of High-voltage Vacuum Circuit Breaker Based on PSO-LSSVM
- High VoltageApparatus Vol. 55, Issue 12, Pages: 248-255(2019)
- 作者机构：
  
  1. 株洲中车时代电气股份有限公司
  2. 沈阳工业大学教育部特种电机与高压电器重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2019.12.037
  CLC： TM561.2
- Published：2019
- 稿件说明：
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[1]姜海龙,李潼清,程浩,侯春光.基于PSO-LSSVM的高压真空断路器弹簧机构机械故障诊断[J].高压电器,2019,55(12):248-255.

JIANG Hailong, LI Tongqing, CHENG Hao, et al. Mechanical Fault Diagnosis for Spring Mechanism of High-voltage Vacuum Circuit Breaker Based on PSO-LSSVM[J]. High voltageapparatus, 2019, 55(12): 248-255.
[1]姜海龙,李潼清,程浩,侯春光.基于PSO-LSSVM的高压真空断路器弹簧机构机械故障诊断[J].高压电器,2019,55(12):248-255. DOI： 10.13296/j.1001-1609.hva.2019.12.037.

JIANG Hailong, LI Tongqing, CHENG Hao, et al. Mechanical Fault Diagnosis for Spring Mechanism of High-voltage Vacuum Circuit Breaker Based on PSO-LSSVM[J]. High voltageapparatus, 2019, 55(12): 248-255. DOI： 10.13296/j.1001-1609.hva.2019.12.037.

摘要

电流检测法在进行高压断路器弹簧机构机械故障诊断时

由于样本数据小导致诊断准确率低。针对这一问题

文中通过模拟各种常见故障和分析不同故障下合闸线圈电流变化规律

提取能够反映断路器工作状态的特征值

并采用最小二乘支持向量机法进行故障诊断。在此基础上

采用粒子群算法不断进行优化以加快诊断速度和准确率。将上述方法进行实例验证

结果表明

文中提出的故障诊断法在相同条件下诊断效果优于广泛应用的BP神经网络诊断法

并且样本空间减小对其诊断结果无明显影响

从而验证文中方法的可行性。

Abstract

When the current detection method is used in the mechanical fault diagnosis of high voltage circuit breaker

the small fault samples would lower its accuracy.To solve this problem

this paper extracts the eigenvalues which can reflect the state of circuit breakers through simulating various common faults and analyzing the change rules of the closing coil current under different faults.And then

it uses least squares support vector machines to diagnose mechanical faults.On this basis

particle swarm optimization is used to optimize the speed and accuracy of the diagnosis.Test results show that the fault diagnosis method proposed in this paper is better than BP neural network diagnosis method under the same conditions.Moreover

the decrease of sample space has no significant influence on the diagnosis

which further verifies the feasibility of this method.

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