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Journal ArticleDOI

Detection of PD utilizing digital signal processing methods. Part 3: Open-loop noise reduction

TLDR
In this paper, open-loop noise reduction techniques can be effective in separating partial discharge (PD) signals from noise, through the use of thresholding of wavelet coefficients, which provides near optimal noise reduction for many classes of signals including PD signals in noise.
Abstract
The paper shows that open-loop noise reduction techniques can be effective in separating partial discharge (PD) signals from noise. Wavelet-based denoising through the use of thresholding of wavelet coefficients provides near optimal noise reduction for many classes of signals including PD signals in noise. The effectiveness of wavelet transform-based noise reduction depends on selection of an appropriate wavelet basis function as well as on careful selection of threshold function and levels.

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Citations
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Journal ArticleDOI

Wavelet-based denoising of partial discharge signals buried in excessive noise and interference

TL;DR: A novel, semi-automatic, and empirical wavelet-based method is proposed to recover PD pulses buried in excessive noise/interference comprising of random, discrete spectral, pulsive, and any combination of these interferences occurring simultaneously and overlapping-in-time with the PD pulses.
Journal ArticleDOI

Interpretation of wavelet analysis and its application in partial discharge detection

TL;DR: The paper demonstrates that the wavelet based denoising method proposed in the paper can be employed in separating PD pulses from electrical noise successfully and can be used in pulse propagation studies of partial discharge in distributed impedance plant to provide enhanced information and further infer the original site of the PD pulse.
Journal ArticleDOI

Automated wavelet selection and thresholding for PD detection

TL;DR: Presents a discussion of some important and unresolved issues related to previous work to provide a more comprehensive understanding of the practicability of wavelet-based denoising.
Journal ArticleDOI

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

TL;DR: 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.
Journal ArticleDOI

An improved methodology for application of wavelet transform to partial discharge measurement denoising

TL;DR: Results prove that, with the proposed methodology, in conjunction with the algorithms proposed by the authors to select optimal mother wavelet and threshold values, significant improvement in denoising effect can be achieved.
References
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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

Adapting to Unknown Smoothness via Wavelet Shrinkage

TL;DR: In this article, the authors proposed a smoothness adaptive thresholding procedure, called SureShrink, which is adaptive to the Stein unbiased estimate of risk (sure) for threshold estimates and is near minimax simultaneously over a whole interval of the Besov scale; the size of this interval depends on the choice of mother wavelet.
Journal ArticleDOI

Decomposition of Hardy functions into square integrable wavelets of constant shape

TL;DR: In this article, the authors studied square integrable coefficients of an irreducible representation of the non-unimodular $ax + b$-group and obtained explicit expressions in the case of a particular analyzing family that plays a role analogous to coherent states (Gabor wavelets) in the usual $L_2 $ -theory.
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