The University of Southampton
University of Southampton Institutional Repository

A lower dimensional feature vector for identification of partial discharges of different origin using time measurements

Evaggorou, Demetres, Kyprianou, Aandreas, Lewin, Paul L., Stavrou, Andreas and Georghiou, George E. (2012) A lower dimensional feature vector for identification of partial discharges of different origin using time measurements Measurement Science and Technology, 23, (5), 055606/1-055606/9. (doi:10.1088/0957-0233/23/5/055606).

Record type: Article

Abstract

Partial discharge (PD) classification into sources of different origin is essential in evaluating the severity of the damage caused by its activity on the insulation of power cables and their accessories. More specifically, some types of PD can be classified as having a detrimental effect on the integrity of the insulation while others can be deemed relatively harmless, rendering the correct classification of different PD types of vital importance to electrical utilities. In this work, a feature vector was proposed based on higher order statistics on selected nodes of the wavelet packet transform (WPT) coefficients of time domain measurements, which can compactly represent the characteristics of different PD sources. To assess its performance, experimental data acquired under laboratory conditions for four different PD sources encountered in power systems were used. The two learning machine methods, namely the support vector machine and the probabilistic neural network, employed as the classification algorithms, achieved overall classification rates of around 98%. In comparison, the utilization of the scaled, raw WPT coefficients as a feature vector resulted in classification accuracy of around 99%, but with a significantly higher number of dimensions (1304 to 16), validating the PD identification ability of the proposed feature. Dimensionality reduction becomes a key factor in online, real-time data collection and processing of PD measurements, reducing the classification effort and the data-storage requirements. Therefore, the proposed method can constitute a potential tool for such online measurements, after addressing issues related to on-site measurements such as the rejection of interference

PDF 0957-0233_23_5_055606.pdf - Version of Record
Restricted to Registered users only
Download (734kB)

More information

Published date: 11 April 2012
Keywords: partial discharges (PD), power system monitoring, wavelet packets transform (WPT), support vector machine (SVM), probabilistic neural network (PNN), feature extractor, pattern recognition
Organisations: EEE

Identifiers

Local EPrints ID: 337082
URI: http://eprints.soton.ac.uk/id/eprint/337082
ISSN: 1361-6501
PURE UUID: 7e345feb-3fc8-4cf3-8b8b-01f77f053e15

Catalogue record

Date deposited: 17 Apr 2012 12:18
Last modified: 18 Jul 2017 06:05

Export record

Altmetrics

Contributors

Author: Demetres Evaggorou
Author: Aandreas Kyprianou
Author: Paul L. Lewin
Author: Andreas Stavrou
Author: George E. Georghiou

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×