Predicting transformers oil parameters

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contributor.author Shaban, K. en_US
contributor.author El-Hag, A. en_US
contributor.author Matveev, A. en_US
date.accessioned 2009-12-24T07:50:10Z en_US
date.available 2009-12-24T07:50:10Z en_US
date.issued 2009 en_US
identifier.citation Shaban, K.; El-Hag, A.; Matveev, A., "Predicting transformers oil parameters," Electrical Insulation Conference, 2009. EIC 2009. IEEE , vol., no., pp.196,199, May 31 2009-June 3 2009 en_US
identifier.uri http://dx.doi.org/10.1109/EIC.2009.5166344 en_US
identifier.uri http://hdl.handle.net/10576/10440 en_US
description.abstract In this paper different configurations of artificial neural networks are applied to predict various transformers oil parameters. The prediction is performed through modeling the relationship between the transformer insulation resistance extracted from the Megger test and the breakdown strength, interfacial tension, acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using two different configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm is implemented. Subsequently, a cascade of these neural networks is deemed to be more promising. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden node combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 83.9% for breakdown voltage, 94.6% for interfacial tension, 56.4% for water content, and 75.4% for oil acidity predictions were obtained by the cascade of neural networks en_US
language.iso en en_US
publisher IEEE en_US
subject learning (artificial intelligence) en_US
subject neural nets en_US
subject power engineering computing en_US
subject transformer oil en_US
title Predicting transformers oil parameters en_US
type Conference Paper en_US


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