Mechanical fault diagnosis method for high voltage circuit breakers based on SS FA ELM algorithm

YIN Xiangguo; HE Yu; BAI Xue; ZHANG Huangsong; QI Kun

doi:10.3969/j.issn.1672-3643.2025.01.010

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Mechanical fault diagnosis method for high voltage circuit breakers based on SS FA ELM algorithm

更新时间：2025-10-13

- Mechanical fault diagnosis method for high voltage circuit breakers based on SS FA ELM algorithm
- Ningxia Electric Power Issue 1, (2025)
- 作者机构：
  
  1. 国网宁夏电力有限公司超高压公司,宁夏,银川,750011
  2. 南京协澳智能控制系统有限公司,江苏,南京,211800
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1672-3643.2025.01.010
  CLC：
- Published Online：15 April 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
YIN Xiangguo, HE Yu, BAI Xue, et al. Mechanical fault diagnosis method for high voltage circuit breakers based on SS FA ELM algorithm[J]. Ningxia electric power, 2025, (1).
DOI：

YIN Xiangguo, HE Yu, BAI Xue, et al. Mechanical fault diagnosis method for high voltage circuit breakers based on SS FA ELM algorithm[J]. Ningxia electric power, 2025, (1). DOI： 10.3969/j.issn.1672-3643.2025.01.010.

摘要

高压断路器通过分合闸控制实现电路的开通与关断，对保障电力系统安全运行具有重要意义。为了解决高压断路器在运行中存在隐性故障难以识别和故障诊断精度不足的问题，提出了一种基于阶段搜索式萤火虫算法优化极限学习机（staged searching firefly algorithm based extreme learning machine

SS FA ELM）的高压断路器机械故障诊断方法。该方法通过提取断路器分合闸线圈的电流特征和时间特征，利用阶段搜索式萤火虫算法对极限学习机的随机权重矩阵进行优化，从而提升模型对故障特征的非线性拟合能力

通过实验对LW30-252高压断路器的故障数据进行诊断测试。结果表明，本文方法的诊断正确率达到96.67%，能够有效提取合闸铁心卡涩、铁心空行程过大和操作机构卡涩等典型机械故障特征，显著提高了高压断路器的故障诊断精度，为保障电力系统稳定运行提供可靠的技术支持。

Abstract

High voltage (HV) circuit breakers are essential for the safe functioning of power systems，as they control the opening and closing of electrical circuits.However

concealed faults in HV circuit breakers are difficult to detect

and fault diagnosis accuracy often falls short.This study proposes a mechanical fault diagnosis method for high voltage circuit breakers based on the staged searching firefly algorithm combined with an extreme learning machine (SS FA ELM).The method optimizes the random weight matrix of the extreme learning machine using the staged searching firefly algorithm

improving the ELMs ability to nonlinearly model the time domain features of coil current in HV circuit breakers.Experimental results based on fault data from the LW30 252 HV circuit breaker show that the proposed method achieves a diagnostic accuracy of 96.67%.It effectively identifies typical mechanical faults

such as iron core jamming

excessive iron core travel

and operating mechanism jamming

significantly enhancing fault diagnosis accuracy.This approach provides reliable technical support for ensuring the stable operation of power systems.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Transformer fault diagnosis based on optimal regular learning machine

Fault diagnosis of converter transformers based on time-frequency maps and parallel CNN-LSTM

Research on operation reliability and fault diagnosis technology of pre-insertion resistors for circuit breakers

Short-term load forecasting using extreme learning machine based on two-layer decomposition technique

Short - term load prediction based on IDE - ELM

Related Author

GONG Jie

JIN Shangting

LUO Weiming

WU Jiekang

QIN Weimei

MA Runze

FENG Xiaowei

CAO Zhi

Related Institution

Extra High Volgage Company,State Grid Ningxia Electric Power Co[JP10].,Ltd[JP10].

State Grid Ningxia Electric Power Co[JP10].,Ltd[JP10].

Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences

AI问答

⁰