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Journal ArticleDOI

A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm

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TLDR
In this article, a new wavelet threshold determination method is proposed with the technique, which has been found to be superior to the other wavelet-based methods, and a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses.
Abstract
Medium and high voltage power cables are widely used in the electrical industry with substantial growth over the last 20-30 years ago, particular in the use of XLPE insulated systems. Ageing of the cable insulation is becoming an increasing problem that requires development of reliable methods for on-line condition assessment. For insulation condition assessment of MV and HV cables, partial discharge (PD) monitoring is one of the most effective techniques. However on-site and on-line PD measurements are affected by electromagnetic interference (EMI) that makes sensitive PD detection very difficult, if not impossible. This paper describes implementation of wavelet transform techniques to reject noise from on-line partial discharge measurements on cables. A new wavelet threshold determination method is proposed with the technique. With implementation of this novel de-noising method, PD measurement sensitivity has been greatly improved. In addition, a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses. Other wavelet threshold de-noising methods are discussed and examined under a noisy environment to compare their performance with the new method proposed here. The method described here has been found to be superior to the other wavelet-based methods

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Citations
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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

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

Scale dependent wavelet selection for de-noising of partial discharge detection

TL;DR: Compared with the correlation-based wavelet selection (CBWS) scheme, the wavelet shrinkage, based on the EBWS, generates significantly smaller waveform distortion and magnitude errors of de-noised PD signals.
Journal ArticleDOI

GPR signal de-noising by discrete wavelet transform

TL;DR: The study found that wavelet de-noising approach outperforms traditional frequency filters such as the elliptic filter and the Daubechies order 6 and Symlet order 6 outperform the Haar and Biorthogonal mother wavelets when de- noising GPR signals by soft thresholding.
Journal ArticleDOI

Flashover process and frequency analysis of the leakage current on insulator model under non-uniform pollution conditions

TL;DR: In this article, the impact of non-uniform pollution carried out under 50 Hz applied voltage on a plane model simulating the 1512 L outdoor insulator largely used by the Algerian Company of Gas and Electric Power (SONELGAZ).
References
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Journal ArticleDOI

A theory for multiresolution signal decomposition: the wavelet representation

TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book

A wavelet tour of signal processing

TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI

De-noising by soft-thresholding

TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.
Journal ArticleDOI

Wavelets and signal processing

TL;DR: A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes, which includes nonstationary signal analysis, scale versus frequency,Wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing.
Book

A Friendly Guide to Wavelets

TL;DR: This paper presents a meta-analysis of Wavelet Transforms using LaSalle's inequality model, which automates the very labor-intensive and therefore time-heavy and expensive process of discrete-time wavelet analysis.
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How to use wavelet?

The method described here has been found to be superior to the other wavelet-based methods