Journal ArticleDOI
Trends in partial discharge pattern classification: a survey
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TLDR
Partial discharge detection, measurement, and classification constitute an important tool for quality assessment of insulation systems utilized in HV power apparatus and cables as mentioned in this paper, and various techniques available for achieving the foregoing task are examined and analyzed; while limited success has been achieved in the identification of simple PD sources, recognition and classification of complex PD patterns associated with practical insulating systems still pose appreciable difficulty.Abstract:
Partial discharge (PD) detection, measurement and classification constitute an important tool for quality assessment of insulation systems utilized in HV power apparatus and cables. The patterns obtained with PD detectors contain characteristic features of the source/class of the respective partial discharge process involved. The recognition of the source from the data represents the classification stage. Usually, this stage consists of a two-step procedure, i.e., extraction of feature vector from the data followed by classification/recognition of the corresponding source. The various techniques available for achieving the foregoing task are examined and analyzed; while limited success has been achieved in the identification of simple PD sources, recognition and classification of complex PD patterns associated with practical insulating systems continue to pose appreciable difficulty.read more
Citations
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Journal ArticleDOI
An overview of state-of-the-art partial discharge analysis techniques for condition monitoring
TL;DR: In this article, a focus of condition monitoring is to detect partial discharge (PD) especially in the early stages to prevent a serious power failure or outage, which is a key indicator of such electrical failure.
Journal ArticleDOI
Partial discharge classifications: Review of recent progress
TL;DR: In this paper, the authors present a literature survey to access the state-of-the-art development in partial discharge classification, which varies greatly in terms of classification techniques used, choice of feature extraction, denoising method, training process, artificial defects created for training purposes and performance assessment.
Journal ArticleDOI
Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network
D. Evagorou,Andreas Kyprianou,Paul Lewin,Andreas Stavrou,Venizelos Efthymiou,A C Metaxas,George E. Georghiou +6 more
TL;DR: In this paper, the moments of the probability density function (PDF) of the wavelet coefficients at various scales, obtained through wavelet packets transformation, were used as a fingerprint for partial discharge (PD) classification.
Journal ArticleDOI
Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources
TL;DR: A novel fuzzy support vector machine (FSVM) and a variety of artificial neural networks (ANNs) are applied in this paper and the classification results reveal that FSVM significantly outperforms a number of ANN algorithms.
Journal ArticleDOI
Application possibilities of artificial neural networks for recognizing partial discharges measured by the acoustic emission method
TL;DR: In this article, a single-direction artificial neural network (SNN) was used to recognize basic partial discharge forms that can occur in paper-oil insulation impaired by aging processes.
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