电力系统保护与控制2024，Vol.52Issue(1)：97-108,12.DOI:10.19783/j.cnki.pspc.230760
基于改进CEEMD和RF的低压串联故障电弧识别方法
Identification method of low voltage series fault arc based on improved CEEMD decomposition and RF
摘要
Abstract
In order to solve the deficiencies that the number of intrinsic mode function component and its frequency band obtained by the decomposition of the complete ensemble empirical mode decomposition(CEEMD)are not fixed,which makes it difficult to accurately extract fault arc characteristics thus leading to low accuracy of fault identification,the T-test and variance contribution rate are introduced to form an improved CEEMD method,and a series fault arc identification method based on improved CEEMD and random forest(RF)is proposed.First,the current signals under different loads are collected by the series arc fault test platform.Second,the improved CEEMD is used to analyze the signal and extract the fault feature quantity.Then,the TreeBagger function is used to reduce the feature dimension to form the feature vector sample set.Finally,a fault arc diagnosis model is constructed combined with RF to classify and identify the sample set.Experimental results show that the improved CEEMD can effectively extract fault features of different load currents,and the identification accuracy of the proposed fault arc recognition method reaches 97.50%.Ablation experiments of the influence of different feature extraction methods and different classification models on the diagnostic results is carried out,the feasibility of the proposed method is further proved.关键词
故障识别/串联故障电弧/改进CEEMD/T检验/方差贡献率/随机森林Key words
fault identification/series fault arc/improved complete ensemble empirical mode decomposition(CEEMD)/T-test/variance contribution rate/random forest引用本文复制引用
江永鑫,陈丽安,郭梦倩,徐子萌..基于改进CEEMD和RF的低压串联故障电弧识别方法[J].电力系统保护与控制,2024,52(1):97-108,12.基金项目
福建省自然科学基金项目资助(2023J011443) This work is supported by the Natural Science Foundation of Fujian Province(No.2023J011443). （2023J011443）
