Mechanical defect identification for gas‐insulated switchgear equipment based on time‐frequency vibration signal analysis  

作　　者：Yao Zhong Jian Hao Ruijin Liao Xupeng Wang Xiping Jiang Feng Wang 

机构地区：[1]State Key Laboratory of Power Transmission Equipment and System Security and New Technology,Chongqing University,Chongqing,China [2]State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute,Chongqing,China [3]Shandong Taikai High‐Volt Switchgear Co.,Ltd.Technology Center,Taian,China

出　　处：《High Voltage》2021年第3期531-542,共12页高电压（英文）

基　　金：National Natural Science Foundation Innovation Research Group Project,Grant/Award Number:51321063;the State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute,Grant/Award Number:2018 Yudian Keji 5#;Chongqing Graduate Scientific Research Innovation Project,Grant/Award Number:CYS20010。

摘　　要：Mechanical defect is an important reason for the failure of gas‐insulated switchgear(GIS)equipment.Based on the time‐frequency characteristic vibration signal analysis on five kinds of mechanical defects,a novel intelligent algorithm model combining complementary ensemble empirical mode decomposition(CEEMD)and genetic al-gorithm improved kernel fuzzy mean clustering(GAKFCM)was proposed to identify the mechanical defect type.First,the mechanical defect platform and detection sys-tem were built.Then CEEMD and IMF sensitivity factors were used to analyse the time‐frequency signal of five kinds of vibration defects,and the feature extraction was performed on the processed vibration signals.Finally,the mechanical vibration defect recognition model was established based on the GAKFCM algorithm and its validity was verified.Results show that the developed detection system can detect mechanical vibration signals sensitively.Singular values,frequency band lines and entropy can reflect the energy attenuation and distribution differences for different type of me-chanical defect vibration signals.The proposed GAKFCM clustering model combining the above vibration feature parameters can effectively find and diagnose the mechanical defect of GIS equipment.Its recognition accuracy reaches 96.74%,especially for the loose contact seat bolts and poor contact failures of the disconnector.

关 键 词：DEFECT analysis VIBRATION 

分 类 号：TM564[电气工程—电器]