Show simple item record

Author Shaban, Khaleden_US
Author El-Hag, Aymanen_US
Author Matveev, Andreien_US
Available date 2009-12-24T07:50:10Zen_US
Publication Date 2009en_US
Publication Name EIC 2009. IEEE Electrical Insulation Conference 2009
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 2009en_US
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 networksen_US
Language enen_US
Publisher IEEEen_US
Subject learning (artificial intelligence)en_US
Subject neural netsen_US
Subject power engineering computingen_US
Subject transformer oilen_US
Title Predicting transformers oil parametersen_US
Type Conference Paperen_US

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record