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An Investigation into Multi-Source Partial Discharge Discrimination within a Power Transformer Model

An Investigation into Multi-Source Partial Discharge Discrimination within a Power Transformer Model
An Investigation into Multi-Source Partial Discharge Discrimination within a Power Transformer Model
This paper investigates a new multi-PD-source discrimination method using machine learning technique, namely support vector machine (SVM). A bushing-tap-RFCT (Radio Frequency Current Transducer) system is used to simulate a power transformer and coupling sensor. Some artificial PD sources are used to generate multi-PD signals. Obtained experimental results from different PD sources are processed using accepted approaches such as ?-q-n patterns, pulse sequence analysis and wavelet transform. The processed data are also used as the input vector of the SVM. Initial results indicate that, by using appropriate feature parameters, the automatic identification results obtained by combining the SVM technique and time domain information are very encouraging.
978-961-6688-00-0
CD-ROM
Hao, L
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Hao, L
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e

Hao, L and Lewin, P L (2007) An Investigation into Multi-Source Partial Discharge Discrimination within a Power Transformer Model. 15th International Symposium on High Voltage Engineering, Ljubjana, Slovenia. 26 - 30 Aug 2007. CD-ROM .

Record type: Conference or Workshop Item (Paper)

Abstract

This paper investigates a new multi-PD-source discrimination method using machine learning technique, namely support vector machine (SVM). A bushing-tap-RFCT (Radio Frequency Current Transducer) system is used to simulate a power transformer and coupling sensor. Some artificial PD sources are used to generate multi-PD signals. Obtained experimental results from different PD sources are processed using accepted approaches such as ?-q-n patterns, pulse sequence analysis and wavelet transform. The processed data are also used as the input vector of the SVM. Initial results indicate that, by using appropriate feature parameters, the automatic identification results obtained by combining the SVM technique and time domain information are very encouraging.

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More information

Published date: 2007
Additional Information: Event Dates: 27-31 August
Venue - Dates: 15th International Symposium on High Voltage Engineering, Ljubjana, Slovenia, 2007-08-26 - 2007-08-30
Organisations: Electronics & Computer Science, EEE

Identifiers

Local EPrints ID: 264393
URI: http://eprints.soton.ac.uk/id/eprint/264393
ISBN: 978-961-6688-00-0
PURE UUID: 9895294c-dc54-4cd2-805a-1653e507fc5e

Catalogue record

Date deposited: 03 Sep 2007
Last modified: 20 Nov 2021 07:46

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Contributors

Author: L Hao
Author: P L Lewin

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