Application of K-Means method to pattern recognition in on-line cable partial discharge monitoring

Xiaosheng Peng; Chengke Zhou; Donald Hepburn; Martin Judd; W H Siew

doi:10.1109/TDEI.2013.6518945

Application of K-Means method to pattern recognition in on-line cable partial discharge monitoring

Xiaosheng Peng, Chengke Zhou, Donald Hepburn, Martin Judd, W H Siew

Research output: Contribution to journal › Article › peer-review

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Abstract

On-line Partial Discharge (PD) monitoring is being increasingly adopted in an effort to improve asset management of the vast network of MV and HV power cables. This paper presents a novel method for autonomous recognition of PD patterns recorded under conditions in which a phase-reference voltage waveform from the HV conductors is not available, as is often the case in on-line PD based insulation condition monitoring. The paper begins with an analysis of two significant challenges for automatic PD pattern recognition. A methodology is then proposed for applying the K-Means method to the task of recognizing PD patterns without phase reference information.

Original language	English
Pages (from-to)	754-761
Number of pages	8
Journal	IEEE Transactions on Dielectrics and Electrical Insulation
Volume	20
Issue number	3
DOIs	https://doi.org/10.1109/TDEI.2013.6518945
Publication status	Published - Jun 2013

Keywords

pattern recognition
PD patterns
K-Means

Documents and Links

10.1109/TDEI.2013.6518945

X. Peng et al (2013) Application of K-Means method to pattern recognition
Originally published in: IEEE Transactions on Dielectrics and Electrical Insulation (2013), 20 (3), pp.754-761. © 2013 IEEE, published by IEEE. Final publisher version of article available at DOI link below.
Accepted author manuscript, 276 KB

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abstract = "On-line Partial Discharge (PD) monitoring is being increasingly adopted in an effort to improve asset management of the vast network of MV and HV power cables. This paper presents a novel method for autonomous recognition of PD patterns recorded under conditions in which a phase-reference voltage waveform from the HV conductors is not available, as is often the case in on-line PD based insulation condition monitoring. The paper begins with an analysis of two significant challenges for automatic PD pattern recognition. A methodology is then proposed for applying the K-Means method to the task of recognizing PD patterns without phase reference information. ",

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AU - Hepburn, Donald

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AU - Siew, W H

N1 - Publication from RCUK funded project (EPSRC) part of RCUK compliance exercise Sept 14. Postprint received 1-10-14. Embargo of 12 months from publication date added to postprint . ET 15-10-14

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N2 - On-line Partial Discharge (PD) monitoring is being increasingly adopted in an effort to improve asset management of the vast network of MV and HV power cables. This paper presents a novel method for autonomous recognition of PD patterns recorded under conditions in which a phase-reference voltage waveform from the HV conductors is not available, as is often the case in on-line PD based insulation condition monitoring. The paper begins with an analysis of two significant challenges for automatic PD pattern recognition. A methodology is then proposed for applying the K-Means method to the task of recognizing PD patterns without phase reference information.

AB - On-line Partial Discharge (PD) monitoring is being increasingly adopted in an effort to improve asset management of the vast network of MV and HV power cables. This paper presents a novel method for autonomous recognition of PD patterns recorded under conditions in which a phase-reference voltage waveform from the HV conductors is not available, as is often the case in on-line PD based insulation condition monitoring. The paper begins with an analysis of two significant challenges for automatic PD pattern recognition. A methodology is then proposed for applying the K-Means method to the task of recognizing PD patterns without phase reference information.

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