Partial discharges location in transformer winding using wavelets and Kullback–Leibler divergence

Guillén, Daniel; Idárraga-Ospina, Gina; Mombello, Enrique E.; Cabral, Sergio H. L.

doi:10.1016/j.epsr.2016.03.020

Cited by 11 publications

(2 citation statements)

References 22 publications

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“…Several techniques for locating P Ds, based mainly on the high frequency current signals captured at the accessible endings of the windings, have been proposed in recent years. As examples, Nafar et al (2011) made use of correlation coefficients between waveforms to determine the point of occurrence of the P Ds; Homaei et al (2014) used unsupervised neuro-fuzzy networks, by means of orthogonal transformation of the received signals; Guillen et al (2016) employed Wavelet-Laplace functions in conjunction with Hellinger's distance calculation; Rahman et al (2016) used a combination of linear and digital filters, based on wavelet coefficients and the main components of the signals; Gonçalves Júnior et al (2018) applied regression models using statistical features from terminal currents of an experimental winding; among others. Although all these methods present significant performances, with a high percentage of hits for the P Ds location, the vast majority was evaluated for only one type of transformer windings, being the applicability in other windings structures thus unknown.…”

Section: Introductionmentioning

confidence: 99%

Multiple Linear Regression Models for Partial Discharge Location Along Transformer Windings

Júnior

Paula

Boaventura

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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More recently, it has been noted an increase in the incidence of failures in power transformers, mainly due to degradation of the solid insulation system. The identification of current pulses related to Partial Discharges (P Ds) has enabled the early detection and location of the solid insulation deteriorated regions, which is very useful to optimize the planning of the maintenance interventions, preventing unexpected power interruptions and reducing downtime. Even though several techniques for P Ds location have been proposed in recent years, limitations can still be noted, such as the inability to specify the discharge location in narrower regions and unproved applicability in windings of different configurations. In view of these, in this work, a P D location method is analysed, based on multiple linear regression models developed in conjunction with stepwise variable selection and analysis of variance methods. The technique is applied from P Ds simulation data, obtained through accurate transformer high frequency modelling, using statistical and principal components features of the terminal windings current signals. The results show that the P Ds can be effectively located, with high accuracy and success rate, for both layer-type and disk-type windings of transformers.

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Section: Introductionmentioning

confidence: 99%

Multiple Linear Regression Models for Partial Discharge Location Along Transformer Windings

Júnior

Paula

Boaventura

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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“…The PDs localization allows maintenance plans to be more oriented and efficient. In recent years, there has been a strong trend towards the development of new methods for the detection and location of partial discharges for various applications in electrical engineering [7,[13][14][15][16][17]. There are existing detection methods, electrical and chemical ones that have proven to be reliable diagnostic tools.…”

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confidence: 99%

Partial discharges location in power transformers using piezoceramic sensors

Danouj

Tahan

David

et al. 2021

IJECE

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The detection and the spatial localization of partial discharges in high-voltage electrical machines are considered as an effective method in predictive maintenance that can provide valuable information on the health of the insulation system and allow to determine accurately the location of the risky insulation elements, which in turn will avoid any premature equipment’s deterioration by scheduling preventive maintenance action. After confirming in a previous published paper the efficiency of a new generation of piezoceramics sensors (high temperature ultrasonic transducers) to detect and characterize partial discharges, we are going to investigate, in this work, a second potential of this technology to locate the partial discharge sources by relying on its ability to detect acoustic signals emitted by partial discharge sources. We will present experimental results, demonstrating the effectiveness of these sensors to locate partial discharges sources and, we will also present an algorithm for calculating the partial discharge foci, based on the acoustic wave flight time.

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A generalization of HH f-divergence

Agahi

Yadollahzadeh

2018

Journal of Computational and Applied Mathematics

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Partial discharges location in transformer winding using wavelets and Kullback–Leibler divergence

Cited by 11 publications

References 22 publications

Multiple Linear Regression Models for Partial Discharge Location Along Transformer Windings

Multiple Linear Regression Models for Partial Discharge Location Along Transformer Windings

Partial discharges location in power transformers using piezoceramic sensors

A generalization of HH f-divergence

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