全球能源互联网2026，Vol.9Issue(1)：112-122,11.DOI:10.19705/j.cnki.issn2096-5125.20250032
基于分合闸线圈电流和触头行程融合的断路器异常辨识方法研究
Research on Abnormal Diagnosis Method for Circuit Breaker Based on the Fusion of Operating Coil Current and Contact Travel
摘要
Abstract
This paper addresses the problem of multi-source signal acquisition and analysis in circuit breaker mechanical abnormal diagnosis.A data acquisition system for closing and opening coil current and contact travel signals is designed,along with corresponding abnormal simulation schemes.Feature extraction is performed using segmented filtering and cyclic difference discrimination,and the CatBoost machine learning algorithm is employed for abnormal diagnosis based on single-source signals.Parameter optimization is achieved using a genetic algorithm(GA),enabling highly accurate diagnosis based on coil current and contact travel signals.Furthermore,linear discriminant analysis(LDA)is used for feature-level fusion to enhance diagnostic performance.The diagnostic results of various fusion methods are analyzed,revealing that both the LDA-GA-CatBoost feature-level fusion method and the improved dempster-shafer(D-S)evidence theory-based decision-level fusion method achieve the highest abnormal diagnosis accuracy of 95.82%.However,the model training time for LDA-GA-CatBoost is only half that of the improved D-S evidence theory method,indicating a clear advantage in practical applications.关键词
断路器/异常诊断/线圈电流/触头行程/机器学习/特征级融合Key words
circuit breaker/abnormal diagnosis/coil current/contact travel/machine learning/feature-level fusion分类
信息技术与安全科学引用本文复制引用
严子成,张昆,刘举,毛雕,孙家涵,袁欢,杨爱军,王小华,荣命哲..基于分合闸线圈电流和触头行程融合的断路器异常辨识方法研究[J].全球能源互联网,2026,9(1):112-122,11.基金项目
三峡金沙江云川水电开发有限公司禄劝乌东德电厂资助(2522302040). Supported by the Luquan Wudongde Power Plant,China Three Gorges Jinshajiang Yunchuan Hydropower Development Co.,Ltd.(No.2522302040). （2522302040）
