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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/61

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contributor.authorPanda, G-
contributor.authorDash, P K-
contributor.authorPradhan, A K-
contributor.authorMeher, S K-
date.accessioned2005-06-17T10:12:47Z-
date.available2005-06-17T10:12:47Z-
date.issued2002-04-
identifier.citationIEEE Transactions on Power Delivery, Vol 17, Iss 2, P 662-667en
identifier.urihttp://hdl.handle.net/2080/61-
descriptionPersonal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en
description.abstractThe slantlet transform (SLT) is an orthogonal discrete wavelet transform (DWT) with two zero moments and with improved time localization. It also retains the basic characteristic of the usual filterbank such as octave band characteristic, a scale dilation factor of two and efficient implementation. However, the SLT is based on the principle of designing different filters for different scales unlike iterated filterbank approaches for the DWT. In this paper a novel approach for power quality data compression using the SLT is presented and its performance in terms of compression ratio (CR), percentage of energy retained and mean square error present in the reconstructed signals is assessed. Varieties of power quality events, which include voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage flicker are used to test the performance of the new approach. Computer simulation results indicate that the SLT offers superior compression performance compared to the conventional discrete cosine transform (DCT) and the DWT based approachesen
format.extent258029 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEEen
subjectdata compressionen
subjectdiscrete wavelet transformsen
subjectpower system transientsen
subjectsignal processingen
titleData compression of power quality events using the slantlet transformen
typeArticleen
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