<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the accurate and high speed techniques for locating of PD sources are required regarding to repair and maintenance. In this paper an attempt has been made to introduce the novel methods based on two different artificial neural networks (ANN) for identifying PD location in the power transformers. In present report Fuzzy ARTmap and Bayesian neural networks are employed for PD locating while using detailed model (DM) for a power transformer for simulation purposes. In present paper PD phenomenon is implemented in different points of transformer winding using threecapacitor model. Then impulse test is applied to transformer terminals in order to use produced current in neutral point for training and test of employed ANNs. In practice obtained current signals include noise components. Thus the performance of Fuzzy ARTmap and Bayesian networks for correct identification of PD location in a noisy condition for detected currents is also investigated. In this paper RBF learning procedure is used for Bayesian network, while Markov chain Monte Carlo (MCMC) method is employed for training of Fuzzy ARTmap network for locating PD in a power transformer winding and results are compared.</p></abstract>

In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the accurate and high speed techniques for locating of PD sources are required regarding to repair and maintenance. In this paper an attempt has been made to introduce the novel methods based on two different artificial neural networks (ANN) for identifying PD location in the power transformers. In present report Fuzzy ARTmap and Bayesian neural networks are employed for PD locating while using detailed model (DM) for a power transformer for simulation purposes. In present paper PD phenomenon is implemented in different points of transformer winding using threecapacitor model. Then impulse test is applied to transformer terminals in order to use produced current in neutral point for training and test of employed ANNs. In practice obtained current signals include noise components. Thus the performance of Fuzzy ARTmap and Bayesian networks for correct identification of PD location in a noisy condition for detected currents is also investigated. In this paper RBF learning procedure is used for Bayesian network, while Markov chain Monte Carlo (MCMC) method is employed for training of Fuzzy ARTmap network for locating PD in a power transformer winding and results are compared.

Locating Pd in Transformers through Detailed Model and Neural Networks

Electrical Engineering Department, Amirkabir University of Technology, Tehran, No. 424, Hafez Ave, 15914, Tehran, Iran

Journal of Electrical Engineering

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

{"article-title":"Locating Pd in Transformers through Detailed Model and Neural Networks"}

In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the...

Locating Pd in Transformers through Detailed Model and Neural Networks

Publié en ligne: 23 avr. 2014

Pages: 75 - 82

DOI: https://doi.org/10.2478/jee-2014-0011

Mots clés
Bayesian network, detailed model (DM), fuzzy ARTmap (FAM) neural network, partial discharge (PD), transformer

© Faculty of Electrical Engineering and Information Technology, Slovak University of Technology

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Locating Pd in Transformers through Detailed Model and Neural Networks

Publié en ligne: 23 avr. 2014

Pages: 75 - 82

DOI: https://doi.org/10.2478/jee-2014-0011

Mots clésBayesian network, detailed model (DM), fuzzy ARTmap (FAM) neural network, partial discharge (PD), transformer

© Faculty of Electrical Engineering and Information Technology, Slovak University of Technology

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Mots clés
Bayesian network, detailed model (DM), fuzzy ARTmap (FAM) neural network, partial discharge (PD), transformer