Identification of Multiple Partial Discharge Sources


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

Download

[img] PDF - Published Version
Restricted to Registered users only

Download (638Kb) | Request a copy

Description/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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Event Dates: 21-24 April 2008
ISBNs: 9781424416219
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science
Faculty of Physical Sciences and Engineering > Electronics and Computer Science > EEE
ePrint ID: 265648
Date Deposited: 29 Apr 2008 13:32
Last Modified: 27 Mar 2014 20:10
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/265648

Actions (login required)

View Item View Item