Classification Method for Mechanical Defects in GIS Equipment Based on Mode Function Analysis and Improved Relevance Vector Machines

作     者：Yao Zhong Jian Hao Xupeng Wang Ruijin Liao Ruilei Gong Yilin Ding Yao Zhong;Jian Hao;Xupeng Wang;Ruijin Liao;Ruilei Gong;Yilin Ding

作者机构：State Key Laboratory of Power Transmission Equipment&System Security and New TechnologyUniversity of ChongqingChongqing 400044China Shandong Taikai High Voltage Swichgear Co.Ltd.Taian 271000China 

出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)

年 卷 期：2023年第9卷第2期

页      面：790-801页

核心收录：

学科分类：0808[工学-电气工程] 080802[工学-电力系统及其自动化] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0807[工学-动力工程及工程热物理] 0805[工学-材料科学与工程（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：supported by the National Natural Science Foundation Innovation Research Group Project (51321063) 

主　　题：GIS IIMF analysis mechanical defect MKFmRVM RPSEMD 

摘      要：Mechanical defects,in gas-insulated switchgear(GIS)equipment,have weak response characteristics,leading to significant difficulties in the classification of ***,this paper proposes a novel mechanical defect feature extraction and classification method that combines independent intrinsic mode function(IIMF)analysis and an improved multikernel mapping fast multi-classification relevance vector machine(MKF-mRVM).Enlightened by the differences in the GIS operating vibration mode,the IIMF series were first obtained based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition(RPSEMD)and modal *** singular value decomposition and time-frequency conversions were performed to construct combined feature ***,multikernel mapping and domain sampling were introduced to improve the calculation speed and recognition accuracy of the mRVM,which was more suitable for on-line *** show that the proposed RPSEMD-MKF-mRVM model achieves a faster training speed(14.23 s)and higher accuracy(98.21%)than other algorithms,and it can adapt to variable loads.