The University of Southampton
University of Southampton Institutional Repository

Use of Machine Learning for Partial Discharge Discrimination

Use of Machine Learning for Partial Discharge Discrimination
Use of Machine Learning for Partial Discharge Discrimination
Partial discharge (PD) measurements are an important tool for assessing the condition of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between PD sources is of great interest to both system utilities and equipment manufacturers. This paper investigates the use of a wide bandwidth PD on-line measurement system to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power systems. Wavelet analysis was applied to pre-process the obtained measurement data. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments indicate that this approach is applicable for use with field measurement data.
115-120
Hao, L
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Swingler, S G
4f13fbb2-7d2e-480a-8687-acea6a4ed735
Hao, L
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Swingler, S G
4f13fbb2-7d2e-480a-8687-acea6a4ed735

Hao, L, Lewin, P L and Swingler, S G (2009) Use of Machine Learning for Partial Discharge Discrimination. At The 11th International Electrical Insulation Conference The 11th International Electrical Insulation Conference, United Kingdom. 26 - 28 May 2009. pp. 115-120.

Record type: Conference or Workshop Item (Paper)

Abstract

Partial discharge (PD) measurements are an important tool for assessing the condition of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between PD sources is of great interest to both system utilities and equipment manufacturers. This paper investigates the use of a wide bandwidth PD on-line measurement system to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power systems. Wavelet analysis was applied to pre-process the obtained measurement data. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments indicate that this approach is applicable for use with field measurement data.

PDF Insucon_2009_southampton_Hao_v2.pdf - Accepted Manuscript
Download (443kB)

More information

Published date: 26 May 2009
Additional Information: Event Dates: 26-28 May 2009
Venue - Dates: The 11th International Electrical Insulation Conference, United Kingdom, 2009-05-26 - 2009-05-28
Organisations: Electronics & Computer Science, EEE

Identifiers

Local EPrints ID: 267552
URI: https://eprints.soton.ac.uk/id/eprint/267552
PURE UUID: b2b156d0-f775-4eb1-9d42-4f55ba8eba3d

Catalogue record

Date deposited: 12 Jun 2009 12:12
Last modified: 18 Jul 2017 07:03

Export record

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 https://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.

×