本文提出多特征融合和IPSO-KNN的有载分接开关机械故障特征选择。首

Proposed feature fusion and IPSO-KNN-load tap mechanical fault feature points selected. First, mRMR principle constructed in time domain, the energy and multi-scale weighted permutation entropy high dimensional set for screening, fault feature subset sensitive; then, the improved PSO sensitive subset of features to optimize the most preferably feature subset using KNN optimal feature subset of the different types of classification; finally, select the normal OLTC contacts, contacts burned off, and the contacts 4 contacts looseness data states, and with FFMWPE, compared FFMPE, SMWPE, FFWPE MFPE and features of the method, the results of analysis of experimental data comparing the effectiveness of the proposed method, while emphasizing the advantages of fusion based MWPE characterized in sensitive feature extraction, feature selection described mRMR necessity.

In this paper, the multi-feature fusion and IPSO-KNN have the mechanical fault characteristics of the carrier tap switch. Firstly, the high-tech collection of time domain, energy and multi-scale weighted arrangement entropy is filtered by mRMR principle to obtain a subset of fault-sensitive features, and then the optimal feature subset is optimized by using the improved particle group algorithm, and the different types of optimal features are classified by KNN. The fault data of the 4 states of OLTC contact normal, contact burn, contact shedding and contact loosening were selected, and compared with the FFMWPE, FFMPE, SMWPE, FFWPE and MFPE characteristic methods, the validity of the proposed method was verified by analyzing and contrasting the results of the experimental data. The advantages of MWPE feature fusion in extracting sensitive features are emphasized, and the necessity of mRMR feature selection is illustrated.

In this paper, multi feature fusion and ipso-knn are proposed to select mechanical fault features of on load tap changer. Firstly, the high Witt feature set of time-domain, energy and multi-scale weighted permutation entropy is screened by using the principle of mrmr to get the fault sensitive feature subset; secondly, the improved particle swarm optimization algorithm is used to optimize the sensitive feature subset to get the optimal feature subset, and KNN is used to classify the different types of optimal feature subset; finally, the OLTC contact is selected as normal and touching Compared with the ffmwpe, ffmpe, smwpe, ffwpe and mfpe feature methods, the experimental data analysis and comparison verify the effectiveness of the proposed method. At the same time, the advantage of feature fusion based on mwpe in extracting sensitive features is emphasized, which shows the necessity of feature selection of mrmr.