电器与能效管理技术Issue(1)：14-22,9.DOI:10.16628/j.cnki.2095-8188.2025.01.003
基于GWO-LSSVM的直流故障电弧诊断方法
DC Fault Arc Diagnosis Method Based on GWO-LSSVM
摘要
Abstract
In order to solve the problem that the identification accuracy of DC arc faults is not high under different working conditions,a gray wolf optimization least squares support vector machine(GWO-LSSVM)is proposed to diagnose DC arc under multi-load conditions.Firstly,the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)is applied to perform the intrinsic mode function(IMF)decomposition on the DC arc current signals obtained from the mixed load of the reference high-speed railway station under the different operating conditions.Secondly,the relevant components are screened and combined with multi-scale permutation entropy(MPE)to construct the feature vectors.Finally,in response to the slow convergence speed of the diagnostic model and the tendency of the model to fall into the local optima,the LSSVM model optimized by GWO is applied for the fault state recognition.The experimental results show that the accuracy reaches 98.33％.By comparing with other algorithms,the efficiency of the proposed method has been confirmed.关键词
直流故障电弧/多尺度排列熵/灰狼优化算法/故障诊断Key words
DC fault arc/multi-scale permutation entropy/grey wolf optimization(GWO)algorithm/fault diagnosis分类
动力与电气工程引用本文复制引用
刘树鑫,刘丙泽,邢朝建,明欣,周厚霖,吕先锋..基于GWO-LSSVM的直流故障电弧诊断方法[J].电器与能效管理技术,2025,(1):14-22,9.基金项目
辽宁省科技重大专项(2020JH1/10100012) （2020JH1/10100012）
国网科技项目(5100-202113396A-0-0-00). （5100-202113396A-0-0-00）
