宁夏电力Issue(1)：73-80,8.DOI:10.3969/j.issn.1672-3643.2025.01.010
基于SS-FA-ELM算法的高压断路器机械故障诊断方法
Mechanical fault diagnosis method for high-voltage circuit breakers based on SS-FA-ELM algorithm
摘要
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 algo-rithm,improving the ELM′s 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
高压断路器/机械故障/极限学习机/故障诊断/萤火虫算法Key words
high-voltage circuit breakers/mechanical faults/extreme learning machine/fault diagnosis/firefly algorithm分类
信息技术与安全科学引用本文复制引用
尹相国,何昱,白雪,张熀松,祁鹍..基于SS-FA-ELM算法的高压断路器机械故障诊断方法[J].宁夏电力,2025,(1):73-80,8.基金项目
宁夏电力有限公司科技项目(5229CG230007) （5229CG230007）
