Wavelet denoising of partial discharge signals and their pattern classification using artificial neural networks and support vector machines

Guzmán, Ian Carlo; Oslinger, Jose Luis; Nieto, Rubén

doi:10.15446/dyna.v84n203.63745

Cited by 9 publications

(9 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In earlier works, several techniques for signal denoising, feature extraction and pattern classification were proposed [10]. Among recent works related to rotating machines, Guzmán et al [11] compared the performance of support vector machines and artificial neural networks (ANNs) for the recognition of three PD sources. Time domain signals were noise filtered with wavelets, and statistical features were extracted.…”

Section: Introductionmentioning

confidence: 99%

Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

et al. 2021

View full text Add to dashboard Cite

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

show abstract

Section: Introductionmentioning

confidence: 99%

Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In order to evaluate the de-noising effect of these wavelet bases more objectively, signal to noise ratio (SNR) and mean square error (MSE) of the de-noised signal were employed as the evaluation indexes (Guzmán et al, 2017). SNR is the proportion of the signal energy and noise energy in the noise signal, MSE is a mean to measure average errors.…”

Section: Performance Evaluation Indexesmentioning

confidence: 99%

Effect of Wavelet De-Noising on the Classification of Pig Behaviour

Jin

Wang

2021

Eng. Agríc.

View full text Add to dashboard Cite

To efficiently eliminate the noise generated by the triaxial accelerometer when collecting pigs' behavioural data, this paper adopted SNR and MSE as the indexes to evaluate the de-noising effect of pigs' acceleration signal under various combinations of wavelet basis, decomposition layer, threshold rule and threshold function. Based on the optimal wavelet parameter combinations, the de-noised data were divided into a training dataset and test dataset to conduct a 3-fold cross validation. The results showed that Db4 wavelet can achieve a satisfactory de-noising effect when used as a wavelet basis for 8 layers wavelet decomposition based on Rigrsure threshold rules and the new improved threshold function. As a result, compared with traditional wavelet hard threshold de-noising, soft threshold de-noising and EMD de-noising method, the improved threshold function improved the stability of signal filtering, which was shown to be more practical, effective and feasible. As such, wavelet de-noising was found to significantly improve the classification accuracy of all four behaviour classes (lying, standing, walking and exploring) considered for this study, and the overall major mean accuracy was improved from 0.680 to 0.826.

show abstract

“…where ψ is known as mother wavelet or reference wavelet, for which a family of wavelet functions can be defined [27]; and ς and τ represent the scale and translation parameters, respectively. The former is related to the expansion/compression of a reference wavelet function in the CWT calculation, whereas the latter denotes the displacement of this reference function.…”

Section: Parameters From Dwtmentioning

confidence: 99%

“…Wavelet transform is a signal processing method that is useful to deal with irregular and non‐periodical signals. A wavelet is characterised as a wave with short duration and small amplitude, being the continuous wavelet transform (CWT) obtained as (5):

CWT false(ς, τ false) = | ς |^{- false(1 / 2 false)} \int_{- normal∞}^{\infty} s false(t false) ψ ()(, \frac{t - τ}{ς}) d t,

where

ψ

is known as mother wavelet or reference wavelet, for which a family of wavelet functions can be defined [27]; and

ς

and

τ

represent the scale and translation parameters, respectively. The former is related to the expansion/compression of a reference wavelet function in the CWT calculation, whereas the latter denotes the displacement of this reference function.…”

Section: Features Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Localisation of inter-layer partial discharges in transformer windings by logistic regression and different features extracted from current signals

Júnior¹,

Paula

Boaventura³

et al. 2020

IET Science, Measurement &Amp; Technology

View full text Add to dashboard Cite

Partial discharge (PD) investigations can identify and localise incipient failures in power transformers early, thus avoiding considerable financial losses. The feature extraction of PD signals is a fundamental step for the development of such location techniques since it directly influences the performance of a location method. This study presents a detailed comparative analysis of four traditional approaches for the obtaining of attributes towards a better set of signal features for the location of PDs. The approaches were critically compared regarding their ability to locate experimentally generated discharges between adjacent layers of a prototype winding. In order to perform such analysis, a localisation structure based on logistic regression models was elaborated, capable of determining both layers and sections of the winding affected by PDs and easily applicable in practice. The results show energy features of wavelet coefficients, obtained through the decomposition of high-frequency current signals acquired at the winding endings, achieve better performance in the PD localisation, accurately indicating discharge occurrence points among layers and sections of the winding.

show abstract

Wavelet denoising of partial discharge signals and their pattern classification using artificial neural networks and support vector machines

Cited by 9 publications

References 47 publications

Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

Effect of Wavelet De-Noising on the Classification of Pig Behaviour

Localisation of inter-layer partial discharges in transformer windings by logistic regression and different features extracted from current signals

Contact Info

Product

Resources

About