The University of Southampton
University of Southampton Institutional Repository

Evaluation of Partial Discharge Denoising using the Wavelet Packets Transform as a Preprocessing Step for Classification

Record type: Conference or Workshop Item (Paper)

The identification of Partial Discharges in high voltage equipment has emerged as one of the most effective condition monitoring methods for assessing the integrity of the equipment under test. The fact that the application of PD monitoring methods is being applied online makes the measurements suffer from noise, inevitable at the measurement point, and reduces the sensitivity of the measurements. Signal processing methods to post process the measurements have been utilised, resulting not only in rejection of the noise and improvement of the sensitivity, but also in improved classification of the PD. A powerful noise rejection technique, the Wavelet Packets Transform (WPT) has been extensively employed for the effective extraction of PD signals from noise. This technique is particularly useful in denoising signals which have transient characteristics. It expands the signal into different bases that are chosen adaptively according to a cost function, transforming the signal into a set of wavelet coefficients. The choice of a cost function has a significant effect on the compact representation of the signal. In this paper after the theory of wavelet packets is first briefly presented, and the denoising performance of the various wavelet packets parameters, such as the wavelet function, the thresholding type, and the cost function to be used is studied through the use of data acquired in a laboratory experimental environment for four types of discharges; namely the corona discharge in air, the internal discharge in oil, the floating discharge in oil and the surface discharge in air. The Symmlet wavelet has been compared with the Daubechies wavelet, both with 8 vanishing moments, the hard thresholding rule has been compared with the soft thresholding rule, and three cost functions have been compared as to their suitability for best basis expansion. Using some predefined criteria to assess their denoising performance the Symmlet 8 has been found to outperform the Daubechies 8 wavelet, the hard thresholding rule to yield better performance than the soft thresholding rule and the Shannon entropy cost function to perform better that the log energy and the norm energy cost functions.

PDF CEIDP2008-000067.pdf - Version of Record
Restricted to Registered users only
Download (658kB)


Evagorou, D, Kyprianou, A, Lewin, P L, Stavrou, A, Efthymiou, V and Georghiou, G E (2008) Evaluation of Partial Discharge Denoising using the Wavelet Packets Transform as a Preprocessing Step for Classification At IEEE 2008 Conference on Electrical Insulation and Dielectric Phenomena, Canada. 26 - 29 Oct 2008. , pp. 387-390.

More information

Published date: 26 October 2008
Additional Information: Event Dates: 26-29 October 2008
Venue - Dates: IEEE 2008 Conference on Electrical Insulation and Dielectric Phenomena, Canada, 2008-10-26 - 2008-10-29
Organisations: Electronics & Computer Science, EEE


Local EPrints ID: 266911
ISBN: 978-1-4244-2549-5
PURE UUID: 2aa90225-fce1-4a05-9e49-ee3b1c772185

Catalogue record

Date deposited: 13 Nov 2008 12:35
Last modified: 18 Jul 2017 07:10

Export record


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

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.