Feature extraction of GIS partial discharge signal based on S‐transform and singular value decomposition. Issue 2 (1st March 2017)

• Record Type: Journal Article
• Authors: Dai, Dangdang; Wang, Xianpei; Long, Jiachuan; Tian, Meng; Zhu, Guowei; Zhang, Jieming
• Is Part Of: IET science, measurement & technology. Volume 11:Issue 2(2017)
• Page Start: 186
• Page End: 193
• Publication Date: 2017-03-01
• Subjects: feature extraction -- partial discharges -- transforms -- singular value decomposition -- support vector machines -- particle swarm optimisation; feature extraction -- GIS partial discharge signal -- S‐transform -- singular value decomposition -- ultra‐high‐frequency signals -- artificial defects -- PD UHF signal -- time–frequency characteristics -- support vector machine -- particle swarm optimisation algorithm -- background noise; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2016.0255
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : Partial discharge (PD) detection and recognition are of great significance to the condition monitoring of gas‐insulated switchgear (GIS). In the current work, ultra‐high‐frequency (UHF) signals induced by PD current pulses are measured and used to represent PD source. For PD classification, feature parameters need to be extracted from UHF signals. Therefore, this study proposes a new feature extraction method that is based on S‐transform (ST) and singular value decomposition (SVD). PD UHF signals generated by four kinds of artificial defects are collected and analysed. ST is used to acquire the joint time–frequency information of the PD UHF signal. SVD is used to acquire the time–frequency characteristics of the UHF signal. Based on the distribution difference of time–frequency characteristics of different kinds of PDs, a 24‐demensional feature vector is finally extracted. Support vector machine optimised by particle swarm optimisation algorithm is employed as classifier to recognise the four kinds of PDs. Results show that the proposed feature extraction method can effectively identify the designed four kinds of PDs even with few samples and strong background noise.