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Erschienen in:
2006 | OriginalPaper | Buchkapitel
An Artificial Neural Network for the Selection of Winding Material in Power Transformers
verfasst von : Eleftherios I. Amoiralis, Pavlos S. Georgilakis, Alkiviadis T. Gioulekas
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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The selection of the winding material in power transformers is an important task, since it has significant impact on the transformer manufacturing cost. This winding material selection has to be checked in every transformer design, which means that for each design, there is a need to optimize the transformer twice and afterwards to select the most economical design. In this paper, an Artificial Neural Network (ANN) is proposed for the selection of the winding material in power transformers, which significantly contributes in the reduction of the effort needed in the transformer design. The proposed ANN architecture provides 94.7% classification success rate on the test set. Consequently, this method is very suitable for industrial use because of its accuracy and implementation speed.