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

Content Provider	IEEE Xplore Digital Library
Author	Evagorou, D. Kyprianou, A. Lewin, P.L. Stavrou, A. Efthymiou, V. Georghiou, G.E.
Copyright Year	2008
Description	Author affiliation: Electr. Authority of Cyprus, Nicosia (Efthymiou, V.; Georghiou, G.E.) \|\| Dept. of Mech. & Manuf. Eng., Univ. of Cyprus, Nicosia (Lewin, P.L.) \|\| Dept. of Electr. & Comput. Eng, Univ. of Cyprus, Nicosia (Evagorou, D.; Kyprianou, A.; Stavrou, A.)
Abstract	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.
Starting Page	387
Ending Page	390
File Size	1559517
Page Count	4
File Format	PDF
ISBN	9781424425488
DOI	10.1109/CEIDP.2008.4772794
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Partial discharges Noise reduction Wavelet packets Wavelet transforms Cost function Noise measurement Signal processing Petroleum Voltage Condition monitoring
Content Type	Text
Resource Type	Article

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