Energy conservation‐based thresholding for effective wavelet denoising of partial discharge signals

Hussein, Ramy; Shaban, Khaled; El-Hag, Ayman H.

doi:10.1049/iet-smt.2016.0168

Cited by 34 publications

(28 citation statements)

References 22 publications

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“…Thus, the performance of signal denoising by WT is depended on the way of threshold value calculation. In this paper, the proposed idea by [10], energy conservation-based method (ECBT), considered as one of the promising methods, is implemented as follows: 1. The original noise-free signal is decomposed to reach the predefined level, J, resulting in AJ and D1-J sub-bands.…”

Section: Thresholding Procedures (Tp)mentioning

confidence: 99%

“…Wideband noises, sometimes called background noises, have a stochastic nature and depend on the measuring system as more sensitive measuring systems are more prone to wideband noise [3,5]. To remove such noise, digital signal processing algorithms-including mathematical morphology [6,7], empirical mode decomposition [8,9], and wavelet transform [2,10]-can be exploited. Mathematical morphology is a time-domain and effective algorithm with a low computational burden but the determination of the type and length of the structure element has always been a challenge [6].…”

Section: Introductionmentioning

confidence: 99%

“…Being one of the most prevalent algorithms, wavelet transform (WT) has long been exploited in PD signal denoising. Although significant progress has been made in the WT based methods, this algorithm is still struggling with a number of challenges such as mother wavelet selection, decomposition level determination, and thresholding procedure [2,3,10].…”

Section: Introductionmentioning

confidence: 99%

“…Since the original noise-free signal is not available to attain E Dj , a pre-defined lookup table is proposed by authors [10], demonstrating the necessity of a prior knowledge of PD signals for implementation of ECBT.…”

mentioning

confidence: 99%

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Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network

Soltani

El-Hag

2019

Energies

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One of the most promising techniques for condition monitoring of high voltage equipment insulation is partial discharge (PD) measurement using radio frequency (RF) antenna. Nevertheless, the accuracy of monitoring, classification, localization, or lifetime estimation could be negatively affected due to the interferences and noises measured simultaneously and contaminate the RF signals. Therefore, to achieve high accuracy of PD assessment, exploiting the denoising algorithms is inevitable. Hence, this paper seeks to introduce a new technique to suppress white noise, the most prevalent type of noise, especially for RF signals. In the proposed method, the ability of artificial neural network (ANN) in curve fitting is applied to denoising of different types of measured RF signals emitted from PD sources including ‘crack’, ‘internal void’, in the insulator discs and ‘sharp points’ from external hardware. The processes of denoising for named signals with the proposed method are carried out, and the obtained results are compared with the outputs of a wavelet transform-based method named energy conversation-based thresholding. In all tested signals, the proposed technique showed superior denoising capability.

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Section: Thresholding Procedures (Tp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network

Soltani

El-Hag

2019

Energies

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“…Wavelets and their generalizations (framelets) have great success in coefficients recovering and have many applications in signal processing and numerical approximations (see Refs. [24][25][26][27]). However, many of these applications are represented by smooth functions that have jump discontinuities.…”

Section: Introductionmentioning

confidence: 99%

On the Gibbs Effect Based on the Quasi-Affine Dual Tight Framelets System Generated Using the Mixed Oblique Extension Principle

Mohammad

2019

Mathematics

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Gibbs effect represents the non-uniform convergence of the nth Fourier partial sums in approximating functions in the neighborhood of their non-removable discontinuities (jump discontinuities). The overshoots and undershoots cannot be removed by adding more terms in the series. This effect has been studied in the literature for wavelet and framelet expansions. Dual tight framelets have been proven useful in signal processing and many other applications where translation invariance, or the resulting redundancy, is very important. In this paper, we will study this effect using the dual tight framelets system. This system is generated by the mixed oblique extension principle. We investigate the existence of the Gibbs effect in the truncated expansion of a given function by using some dual tight framelets representation. We also give some examples to illustrate the results.

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Measurement of Partial Discharges

2023

Partial Discharges (PD)

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Energy conservation‐based thresholding for effective wavelet denoising of partial discharge signals

Cited by 34 publications

References 22 publications

Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network

Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network

On the Gibbs Effect Based on the Quasi-Affine Dual Tight Framelets System Generated Using the Mixed Oblique Extension Principle

Measurement of Partial Discharges

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