Visible to the public Feature Fingerprint Extraction and Abnormity Diagnosis Method of the Vibration on the GIS

TitleFeature Fingerprint Extraction and Abnormity Diagnosis Method of the Vibration on the GIS
Publication TypeConference Paper
Year of Publication2020
AuthorsWang, H., Yang, J., Wang, X., Li, F., Liu, W., Liang, H.
Conference Name2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE)
Date PublishedSept. 2020
PublisherIEEE
ISBN Number978-1-7281-5511-1
KeywordsAcoustic Fingerprints, Acoustics, Circuit faults, composability, condition monitoring and fault diagnosis, feature extraction, Fingerprint recognition, Gas insulated switchgear (GIS), Gas insulation, Human Behavior, mechanical vibration, pubcrawl, resilience, Resiliency, vibration fingerprint, Vibrations, wavelet transforms
Abstract

Mechanical faults of Gas Insulated Switchgear (GIS) often occurred, which may cause serious losses. Detecting vibration signal was effective for condition monitoring and fault diagnosis of GIS. The vibration characteristic of GIS in service was detected and researched based on a developed testing system in this paper, and feature fingerprint extraction method was proposed to evaluate vibration characteristics and diagnose mechanical defects. Through analyzing the spectrum of the vibration signal, we could see that vibration frequency of operating GIS was about 100Hz under normal condition. By means of the wavelet transformation, the vibration fingerprint was extracted for the diagnosis of mechanical vibration. The mechanical vibration characteristic of GIS including circuit breaker and arrester in service was detected, we could see that the frequency distribution of abnormal vibration signal was wider, it contained a lot of high harmonic components besides the 100Hz component, and the vibration acoustic fingerprint was totally different from the normal ones, that is, by comparing the frequency spectra and vibration fingerprint, the mechanical faults of GIS could be found effectively.

URLhttps://ieeexplore.ieee.org/document/9279652
DOI10.1109/ICHVE49031.2020.9279652
Citation Keywang_feature_2020