The University of Southampton
University of Southampton Institutional Repository

Multiple partial discharge source discrimination in a high voltage transformer winding

Multiple partial discharge source discrimination in a high voltage transformer winding
Multiple partial discharge source discrimination in a high voltage transformer winding
Partial discharge (PD) analysis is an important technique for diagnosis and online monitoring of transformer insulation systems. PD within transformer windings may be due to several causes such as manufacturing defects, degradation of the primary insulation or contamination of the oil. The degradation processes occurring in dielectric insulation components can lead to development of different types of PD source [1], [2]. Multiple PD sources induced by different defects can be simultaneously present within the transformer winding. Therefore, it is necessary to develop tools to separate measurement data from multiple PD sources in order to facilitate separate PD source identification and location to allow accurate condition assessment of the winding.
Conventional techniques for multiple PD source separation are based on analysis of the phase resolved PD (PRPD) pattern [3], [4]. However, in the presence of multiple PD sources, PRPD patterns generated by different PD sources can significantly overlap each other [5], [6]. By using PRPD pattern-based techniques, multiple PD sources cannot easily be separated as one PD source can be entirely immersed in the patterns of the other sources. Recently, there have been a number of techniques reported for separating multiple PD sources such as through the use of support vector machines (SVMs) or artificial neural networks (ANNs) [3], [7].
This paper reports on a technique that relies on an initial assumption that different PD sources generate different waveform characteristics [8]. The measured discharge current signals from both ends of a transformer winding were decomposed using the Mathematical Morphology (MM) decomposition technique. Then, the energy distribution for each signal for each structure element (SE) length of MM was determined. The OPTICS algorithm was then used as a density based clustering technique to reveal different clusters of signals with similar calculated energy distributions.
215-220
Nik Ali, Nik Hakimi, Bin
91f9aa04-0cd9-4d62-896f-97584753886d
Rapisarda, Paolo
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Nik Ali, Nik Hakimi, Bin
91f9aa04-0cd9-4d62-896f-97584753886d
Rapisarda, Paolo
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e

Nik Ali, Nik Hakimi, Bin, Rapisarda, Paolo and Lewin, Paul (2017) Multiple partial discharge source discrimination in a high voltage transformer winding. International Insulation Conference, Birmingham, United Kingdom. 16 - 18 May 2017. pp. 215-220 .

Record type: Conference or Workshop Item (Paper)

Abstract

Partial discharge (PD) analysis is an important technique for diagnosis and online monitoring of transformer insulation systems. PD within transformer windings may be due to several causes such as manufacturing defects, degradation of the primary insulation or contamination of the oil. The degradation processes occurring in dielectric insulation components can lead to development of different types of PD source [1], [2]. Multiple PD sources induced by different defects can be simultaneously present within the transformer winding. Therefore, it is necessary to develop tools to separate measurement data from multiple PD sources in order to facilitate separate PD source identification and location to allow accurate condition assessment of the winding.
Conventional techniques for multiple PD source separation are based on analysis of the phase resolved PD (PRPD) pattern [3], [4]. However, in the presence of multiple PD sources, PRPD patterns generated by different PD sources can significantly overlap each other [5], [6]. By using PRPD pattern-based techniques, multiple PD sources cannot easily be separated as one PD source can be entirely immersed in the patterns of the other sources. Recently, there have been a number of techniques reported for separating multiple PD sources such as through the use of support vector machines (SVMs) or artificial neural networks (ANNs) [3], [7].
This paper reports on a technique that relies on an initial assumption that different PD sources generate different waveform characteristics [8]. The measured discharge current signals from both ends of a transformer winding were decomposed using the Mathematical Morphology (MM) decomposition technique. Then, the energy distribution for each signal for each structure element (SE) length of MM was determined. The OPTICS algorithm was then used as a density based clustering technique to reveal different clusters of signals with similar calculated energy distributions.

Text
Insucon paper - Version of Record
Restricted to Registered users only
Download (1MB)
Request a copy

More information

Published date: 16 May 2017
Venue - Dates: International Insulation Conference, Birmingham, United Kingdom, 2017-05-16 - 2017-05-18

Identifiers

Local EPrints ID: 413347
URI: http://eprints.soton.ac.uk/id/eprint/413347
PURE UUID: aa413e0e-0621-4f52-a724-5596096e280f

Catalogue record

Date deposited: 22 Aug 2017 16:31
Last modified: 19 Jul 2019 18:03

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 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.

×