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Partial Discharge Source Discrimination using a Support Vector Machine

Partial Discharge Source Discrimination using a Support Vector Machine
Partial Discharge Source Discrimination using a Support Vector Machine
Partial discharge (PD) measurements are an important tool for assessing the health of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between 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 consisting of a radio frequency current transducer (RFCT) sensor, a digital storage oscilloscope and a high performance personal computer to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power system apparatus. Wavelet analysis was applied to pre-process measurement data obtained from the wide bandwidth PD sensor. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments where the trained SVM was tested using measurement data from the RFCT as opposed to conventional measurement data indicate that this approach has a robust performance and has great potential for use with field measurement data
1070-9878
189-197
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. (2010) Partial Discharge Source Discrimination using a Support Vector Machine. IEEE Transactions on Dielectrics & Electrical Insulation, 17 (1), 189-197. (doi:10.1109/TDEI.2010.5412017).

Record type: Article

Abstract

Partial discharge (PD) measurements are an important tool for assessing the health of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between 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 consisting of a radio frequency current transducer (RFCT) sensor, a digital storage oscilloscope and a high performance personal computer to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power system apparatus. Wavelet analysis was applied to pre-process measurement data obtained from the wide bandwidth PD sensor. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments where the trained SVM was tested using measurement data from the RFCT as opposed to conventional measurement data indicate that this approach has a robust performance and has great potential for use with field measurement data

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Published date: 1 February 2010
Organisations: Electronics & Computer Science, EEE

Identifiers

Local EPrints ID: 268525
URI: http://eprints.soton.ac.uk/id/eprint/268525
ISSN: 1070-9878
PURE UUID: c2661cb9-871c-4ca9-8178-b508a2d886e8

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Date deposited: 22 Feb 2010 15:46
Last modified: 25 Nov 2021 17:05

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Contributors

Author: L. Hao
Author: P.L. Lewin

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