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Comparison of two partial discharge classification methods

Comparison of two partial discharge classification methods
Comparison of two partial discharge classification methods
Two signal classification methods have been examined to discover their suitability for the task of partial discharge (PD) identification. An experiment has been designed to artificially mimic signals produced by a range of PD sources that are known to occur within high voltage (HV) items of plant. The bushing tap point of a large Auto-transformer has been highlighted as a possible point on which to attach PD sensing equipment and is utilized in this experiment. Artificial PD signals are injected into the HV electrode of the bushing itself and a high frequency current transformer (HFCT) is used to monitor the current between the tap-point and earth. The experimentally produced data was analyzed using two different signal processing algorithms and their classification performance compared. The signals produced by four different artificial PD sources (surface discharge in air, corona discharge in air, floating discharge in oil and internal discharge in oil) have been processed, then classified using two machine learning techniques, namely the support vector machine (SVM) and probabilistic neural network (PNN). The feature extraction algorithms involve performing wavelet packet analysis on the PD signals recorded over a single power cycle. The dimensionality of the data has been reduced by finding the first four moments of the probability density function (Mean, Standard deviation, Skew and Kurtosis) of the wavelet packet coefficients to produce a suitable feature vector. Initial results indicate that very high identification rates are possible with the SVM able to classify PD signals with a slightly higher accuracy than a PNN.
978-1-4244-6300-8
Hunter, J.A.
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Lewin, P.L.
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Evagorou, D.
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Kyprianou, A.
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Georghiou, G.E.
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Hunter, J.A.
dae3e13b-a97e-4e81-a617-20ab6965da3c
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Evagorou, D.
0971dcb8-7498-499a-846c-c571414be103
Kyprianou, A.
5e95e090-e0af-43b6-b31e-09d831696092
Georghiou, G.E.
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Hunter, J.A., Lewin, P.L., Evagorou, D., Kyprianou, A. and Georghiou, G.E. (2010) Comparison of two partial discharge classification methods. IEEE 2010 International Symposium on Electrical Insulation, San Diego, California, United States. 05 - 08 Jun 2010.

Record type: Conference or Workshop Item (Paper)

Abstract

Two signal classification methods have been examined to discover their suitability for the task of partial discharge (PD) identification. An experiment has been designed to artificially mimic signals produced by a range of PD sources that are known to occur within high voltage (HV) items of plant. The bushing tap point of a large Auto-transformer has been highlighted as a possible point on which to attach PD sensing equipment and is utilized in this experiment. Artificial PD signals are injected into the HV electrode of the bushing itself and a high frequency current transformer (HFCT) is used to monitor the current between the tap-point and earth. The experimentally produced data was analyzed using two different signal processing algorithms and their classification performance compared. The signals produced by four different artificial PD sources (surface discharge in air, corona discharge in air, floating discharge in oil and internal discharge in oil) have been processed, then classified using two machine learning techniques, namely the support vector machine (SVM) and probabilistic neural network (PNN). The feature extraction algorithms involve performing wavelet packet analysis on the PD signals recorded over a single power cycle. The dimensionality of the data has been reduced by finding the first four moments of the probability density function (Mean, Standard deviation, Skew and Kurtosis) of the wavelet packet coefficients to produce a suitable feature vector. Initial results indicate that very high identification rates are possible with the SVM able to classify PD signals with a slightly higher accuracy than a PNN.

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More information

Published date: 6 June 2010
Additional Information: CD ROM
Venue - Dates: IEEE 2010 International Symposium on Electrical Insulation, San Diego, California, United States, 2010-06-05 - 2010-06-08
Organisations: EEE

Identifiers

Local EPrints ID: 271225
URI: http://eprints.soton.ac.uk/id/eprint/271225
ISBN: 978-1-4244-6300-8
PURE UUID: 2d9c2872-05e0-48a0-b0ab-eb63f3cd9e22

Catalogue record

Date deposited: 06 Jun 2010 19:58
Last modified: 08 Nov 2021 21:52

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Contributors

Author: J.A. Hunter
Author: P.L. Lewin
Author: D. Evagorou
Author: A. Kyprianou
Author: G.E. Georghiou

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