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Identification of PD defect typologies using a support vector machine

Identification of PD defect typologies using a support vector machine
Identification of PD defect typologies using a support vector machine
The Support Vector Machine (SVM) has been adopted here to identify four different Partial Discharge (PD) sources that can affect the insulation system of AC rotating machines. A number of Roebel bars were prepared to generate bar-to-finger, corona and slot PD in addition to the distributed micro-voids that are typical of this insulation type. PD measurements were performed using different set-up conditions, defect locations and voltage levels in order to produce examples of PD activity that represent the same source under a range of conditions. The SVM was trained to differentiate between the inherent features (global and derived parameters) of the phase resolved PD (PRPD) distributions produced by each discharge source. In order to achieve the optimum source classification accuracy, different combinations of distribution features were used to produce a range of SVM models to identify which parameters were influenced by the measurement conditions. A cross validation technique has been used to obtain the highest testing accuracy. Moreover, results obtained using raw data and normalized parameters, were also compared to obtain the best identification performance of the given defect typologies.
978-1-4673-4739-6
333-336
Lewin, P.L.
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Hunter, J.A.
dae3e13b-a97e-4e81-a617-20ab6965da3c
Hao, L
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Contin, A
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Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Hunter, J.A.
dae3e13b-a97e-4e81-a617-20ab6965da3c
Hao, L
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Contin, A
9c8d180d-d037-4d75-aa8a-1dcfa558aaeb

Lewin, P.L., Hunter, J.A., Hao, L and Contin, A (2013) Identification of PD defect typologies using a support vector machine. IEEE 2013 Electrical Insulation Conference, Ottawa, Canada. 01 - 04 Jun 2013. pp. 333-336 .

Record type: Conference or Workshop Item (Paper)

Abstract

The Support Vector Machine (SVM) has been adopted here to identify four different Partial Discharge (PD) sources that can affect the insulation system of AC rotating machines. A number of Roebel bars were prepared to generate bar-to-finger, corona and slot PD in addition to the distributed micro-voids that are typical of this insulation type. PD measurements were performed using different set-up conditions, defect locations and voltage levels in order to produce examples of PD activity that represent the same source under a range of conditions. The SVM was trained to differentiate between the inherent features (global and derived parameters) of the phase resolved PD (PRPD) distributions produced by each discharge source. In order to achieve the optimum source classification accuracy, different combinations of distribution features were used to produce a range of SVM models to identify which parameters were influenced by the measurement conditions. A cross validation technique has been used to obtain the highest testing accuracy. Moreover, results obtained using raw data and normalized parameters, were also compared to obtain the best identification performance of the given defect typologies.

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More information

Published date: 2 June 2013
Venue - Dates: IEEE 2013 Electrical Insulation Conference, Ottawa, Canada, 2013-06-01 - 2013-06-04
Organisations: EEE

Identifiers

Local EPrints ID: 353266
URI: http://eprints.soton.ac.uk/id/eprint/353266
ISBN: 978-1-4673-4739-6
PURE UUID: 0818579c-4bf7-4eb3-b3d1-84a611317d69

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Date deposited: 03 Jun 2013 20:12
Last modified: 20 Nov 2021 14:51

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Contributors

Author: P.L. Lewin
Author: J.A. Hunter
Author: L Hao
Author: A Contin

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