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Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network

Evagorou, D, Kyprianou, A, Lewin, P L, Stavrou, A, Efthymiou, V, Metaxas, A C and Georghiou, G E (2010) Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network IET Science, Measurement and Technology, 4, (3), pp. 177-192.

Record type: Article


Partial discharge (PD) classification in power cable accessories and high voltage equipment in general is essential in evaluating the severity of the damage in the insulation. In this article, the PD classification was realised as a two-fold process. Firstly, measurements taken from a high-frequency current transformer (HFCT)sensor were represented as features by means of a transformation to the classifier and secondly, the probabilistic neural network (PNN) classifier itself was capable of effectively recognising features coming from different types of discharges. The feature that was used as a fingerprint for PD characterisation was extracted from the moments of the probability density function (PDF) of the wavelet coefficients at various scales, obtained through the wavelet packets transformation. The PNN classifier was used to classify the PDs and assess the suitability of this feature vector in PD classification. Four types of artificial PDs were created in a high voltage laboratory, namely corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air, at different applied voltages, and were used to train the PNN algorithm. The results obtained here (97.49, 91.9, 100 and 99.8% for the corona, the floating, the internal and the surface discharges, respectively) are very encouraging for the use of PNN in PD classification with this particular feature vector. This article suggests a feature extraction and classification algorithm for PD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, and achieved very high levels of classification.

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Published date: 1 May 2010
Organisations: Electronics & Computer Science, EEE


Local EPrints ID: 271494
ISSN: 1751-8822
PURE UUID: 784fa007-2f39-497c-a721-642ae545720b

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Date deposited: 20 Aug 2010 12:45
Last modified: 18 Jul 2017 06:42

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Author: D Evagorou
Author: A Kyprianou
Author: P L Lewin
Author: A Stavrou
Author: V Efthymiou
Author: A C Metaxas
Author: G E Georghiou

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