NDLI: Denoising and feature extraction in PD based cable condition monitoring systems

Content Provider	IEEE Xplore Digital Library
Author	Xiaosheng Peng Chengke Zhou Hepburn, D.M. Xiaodi Song
Copyright Year	2010
Description	Author affiliation: Glasgow Caledonian University, UK, G4 0BA (Xiaosheng Peng; Chengke Zhou; Hepburn, D.M.; Xiaodi Song)
Abstract	There has been increasing application of on-line partial discharge (PD) based cable insulation condition monitoring among utilities worldwide due to the ability of on-line PD monitoring to allow incipient insulation faults to be detected and aged cable replacement program to be prioritised. However the application is also accompanied with a number of challenges. Data from on-line PD monitoring systems shows presence of higher levels of interference, including sinusoidal RF noise, switching pulses, PD from local plant, radio and power line carrier communication systems, etc. The biggest challenge associated with on-line cable PD monitoring is to distinguish PD generated in cable insulation from noisy raw data, which requires not only application of data denoising techniques but also feature extraction techniques to differentiate signals coming from different sources based on their characteristics. This paper aims to overcome the above-mentioned challenge. Following a brief introduction the paper introduces an effective denoising technique involving the adaptive second generation wavelet transform (ASGWT). To describe the various PD pulses, which the authors have observed from on-line cable PD monitoring systems, methods for PD feature extraction are discussed. These include analysis of raw PD signal, phase-resolved PD pattern, etc. Finally, based on data denoising and feature extraction, signal classification for an on-site PD testing experiment is introduced.
Starting Page	1
Ending Page	4
File Size	341804
Page Count	4
File Format	PDF
ISBN	9781424494682
ISSN	00849162
e-ISBN	9781424494712
DOI	10.1109/CEIDP.2010.5723938
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Partial discharges Noise reduction Interference Monitoring Feature extraction Wavelet transforms Noise Ф- V- n pattern partial discharge medium voltage cable on-line monitoring data SGWT decision tree
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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