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Identification of Multiple Partial Discharge Sources

Record type: Conference or Workshop Item (Paper)

Partial discharge (PD) measurements are an important tool for assessing the health of power equipment. Different PD may have different effects on the insulation performance of power apparatus. Therefore, identification of 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 which consists of a wide bandwidth sensor, a sophisticated digital signal oscilloscope and a high performance personal computer to facilitate automatic PD source identification. Wavelet analysis was applied to the obtained raw measurement data. The pre-processed data was then processed using correlation analysis. The obtained results have also been processed by accepted approaches, such as phase resolved information. A machine learning technique, namely the support vector machine (SVM) has been used to identify between the different PD sources.

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Citation

Hao, L, Lewin, P L and Swingler, S G (2008) Identification of Multiple Partial Discharge Sources At 2008 International Conference on Condition Monitoring and Diagnosis, China. 21 - 24 Apr 2008. , pp. 118-121.

More information

Published date: 21 April 2008
Additional Information: Event Dates: 21-24 April 2008
Venue - Dates: 2008 International Conference on Condition Monitoring and Diagnosis, China, 2008-04-21 - 2008-04-24
Organisations: Electronics & Computer Science, EEE

Identifiers

Local EPrints ID: 265648
URI: http://eprints.soton.ac.uk/id/eprint/265648
ISBN: 978-1-4244-1621-9
PURE UUID: 3c042156-9849-405f-9a96-16c56963b9df

Catalogue record

Date deposited: 29 Apr 2008 13:32
Last modified: 18 Jul 2017 07:24

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Contributors

Author: L Hao
Author: P L Lewin
Author: S G Swingler

University divisions

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