Time–frequency analysis of PD‐induced UHF signal in GIS and feature extraction using invariant moments. Issue 2 (1st March 2018)

• Record Type: Journal Article
• Authors: Li, Xi; Wang, Xiaohua; Xie, Dingli; Wang, Xinqiao; Yang, Aijun; Rong, Mingzhe
• Is Part Of: IET science, measurement & technology. Volume 12:Issue 2(2018)
• Page Start: 169
• Page End: 175
• Publication Date: 2018-03-01
• Subjects: time‐frequency analysis -- gas insulated switchgear -- partial discharge measurement -- feature extraction -- signal detection -- signal classification -- UHF detectors -- UHF measurement -- Fourier transforms -- support vector machines; Time–frequency analysis -- PD‐induced UHF signal detection -- feature extraction -- ultrahigh‐frequency method -- partial discharge detection -- gas‐insulated switchgear -- propagation characteristics analysis -- PD pattern classification -- PD‐induced UHF signal acquisition -- internal UHF sensor -- L‐shaped GIS -- short‐time Fourier transform method -- UHF signal processing -- Hu invariant moment -- energy density distribution extraction -- support vector machine classifier -- UHF signal classification -- voltage 252 kV; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2017.0287
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : The ultra‐high‐frequency (UHF) method is efficient in partial discharges (PDs) detection in gas‐insulated switchgear (GIS). The features extraction of UHF signals is significant for propagation characteristics analysis and PD pattern classification. The PD‐induced UHF signals are acquired by the internal UHF sensors in an actual 252 kV L‐shaped GIS. The short‐time Fourier transform method is applied to process UHF signals and describe the propagation characteristics in L‐shaped GIS. Hu's invariant moments of energy density distribution are extracted as features in time–frequency plane. The features are utilised to discriminate different PD defect patterns in actual GIS model by the support vector machine classifier and achieve good results. Finally, a novel system of features extraction and classification of UHF signals is summarised.