Identification of Multiple Partial Discharge Sources
Identification of Multiple Partial Discharge Sources
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.
978-1-4244-1621-9
118-121
Hao, L
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Swingler, S G
4f13fbb2-7d2e-480a-8687-acea6a4ed735
21 April 2008
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
(2008)
Identification of Multiple Partial Discharge Sources.
2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China.
21 - 24 Apr 2008.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
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.
Text
A2-11.pdf
- Version of Record
Restricted to Registered users only
Request a copy
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, Beijing, 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: 15 Mar 2024 02:43
Export record
Contributors
Author:
L Hao
Author:
P L Lewin
Author:
S G Swingler
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