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作 者:尹相国 何昱 白雪 张熀松 祁鹍 YIN Xiangguo;HE Yu;BAI Xue;ZHANG Huangsong;QI Kun(Ultra High Voltage Company,State Grid Ningxia Electric Power Co.,Ltd.,Yinchuan Ningxia 750011,China;Nanjing ODES Intelligent Control System Co.,Ltd.,Nanjing Jiangsu 211800,China)
机构地区:[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
出 处:《宁夏电力》2025年第1期73-80,共8页Ningxia Electric Power
基 金:宁夏电力有限公司科技项目(5229CG230007)。
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。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 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.
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