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Performance of the support vector machine partial discharge classification method to noise contamination using phase synchronous measurements

Performance of the support vector machine partial discharge classification method to noise contamination using phase synchronous measurements
Performance of the support vector machine partial discharge classification method to noise contamination using phase synchronous measurements
The Support Vector Machine (SVM) method has been used with success in classifying Partial Discharge (PD) data of different sources. In this work it was investigated whether the previous success of the Support Vector Machine (SVM) could be extended to the case where a PD measurement was corrupted by Additive White Gaussian Noise (AWGN). Data was collected from experiments using PDs of different sources under controlled laboratory conditions at the Tony Davies High Voltage Laboratory, University of Southampton. Artificial PD signals were injected into the HV electrode of a bushing and a high frequency current transformer (HFCT) was used to monitor the current between the tap-point and earth. The signals produced by four different artificial PD sources (corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air) were acquired using the peak detection mode of the oscilloscope and were processed to extract the feature that was used by each algorithm. The feature extraction algorithm involved the use of the Wavelet Packet Transform (WPT) on phase synchronous measurements corrupted by artificial AWGN. Once the SVM was trained using part of the data acquired in the laboratory then the remaining data was corrupted by noise of two different amplitudes, giving SNRs of 7 dB and 3dB. These noisy data were classified using the SVM and the classification results were recorded. This procedure validated the SVM as an effective classification method that can be trained using laboratory noise free PD signals which can subsequently be used to classify field on-line measurements that have been corrupted with noise.
458-461
Evagorou, D.
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Kyprianou, A.
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Stavrou, A.
98cd24c5-0ed3-4b39-ad0c-21bb5234f21b
Hunter, J.A.
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Hao, L.
7617ddd7-90e9-44b2-af12-717babd9c0ee
Lewin, P.L.
94fb642b-8d25-42a8-a262-95d60dba2805
Georghiou, G.E.
9cfa57e6-8aea-433f-8be2-449839fc6b1d
Evagorou, D.
0971dcb8-7498-499a-846c-c571414be103
Kyprianou, A.
5e95e090-e0af-43b6-b31e-09d831696092
Stavrou, A.
98cd24c5-0ed3-4b39-ad0c-21bb5234f21b
Hunter, J.A.
dae3e13b-a97e-4e81-a617-20ab6965da3c
Hao, L.
7617ddd7-90e9-44b2-af12-717babd9c0ee
Lewin, P.L.
94fb642b-8d25-42a8-a262-95d60dba2805
Georghiou, G.E.
9cfa57e6-8aea-433f-8be2-449839fc6b1d

Evagorou, D., Kyprianou, A., Stavrou, A., Hunter, J.A., Hao, L., Lewin, P.L. and Georghiou, G.E. (2010) Performance of the support vector machine partial discharge classification method to noise contamination using phase synchronous measurements. 2010 Conference on Electrical Insulation and Dielectric Phenomena, Purdue University, West Lafayette, Indiana, United States. 17 - 20 Oct 2010. pp. 458-461 .

Record type: Conference or Workshop Item (Paper)

Abstract

The Support Vector Machine (SVM) method has been used with success in classifying Partial Discharge (PD) data of different sources. In this work it was investigated whether the previous success of the Support Vector Machine (SVM) could be extended to the case where a PD measurement was corrupted by Additive White Gaussian Noise (AWGN). Data was collected from experiments using PDs of different sources under controlled laboratory conditions at the Tony Davies High Voltage Laboratory, University of Southampton. Artificial PD signals were injected into the HV electrode of a bushing and a high frequency current transformer (HFCT) was used to monitor the current between the tap-point and earth. The signals produced by four different artificial PD sources (corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air) were acquired using the peak detection mode of the oscilloscope and were processed to extract the feature that was used by each algorithm. The feature extraction algorithm involved the use of the Wavelet Packet Transform (WPT) on phase synchronous measurements corrupted by artificial AWGN. Once the SVM was trained using part of the data acquired in the laboratory then the remaining data was corrupted by noise of two different amplitudes, giving SNRs of 7 dB and 3dB. These noisy data were classified using the SVM and the classification results were recorded. This procedure validated the SVM as an effective classification method that can be trained using laboratory noise free PD signals which can subsequently be used to classify field on-line measurements that have been corrupted with noise.

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Published date: 17 October 2010
Venue - Dates: 2010 Conference on Electrical Insulation and Dielectric Phenomena, Purdue University, West Lafayette, Indiana, United States, 2010-10-17 - 2010-10-20
Organisations: EEE

Identifiers

Local EPrints ID: 271645
URI: http://eprints.soton.ac.uk/id/eprint/271645
PURE UUID: 4b134880-d1a6-459c-8927-402221ebded5

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Date deposited: 20 Oct 2010 13:34
Last modified: 14 Mar 2024 09:36

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Contributors

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

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