Application Research Based on Artificial Neural Network (ANN) to Predict No-Load Loss for Transformer's Design

Citations Over TimeTop 23% of 2011 papers

Abstract

Transformer is one of the vital components in electrical network which play important role in the power system. The continuous performance of transformers is necessary for retaining the network reliability, forecasting its costs for manufacturer and industrial companies. The major amounts of transformer costs are related to its no-load loss, so the cost estimation processes of transformers are based on reduction of no-load loss. This paper presents a new method for classification of transformer no-load losses. It is shown that ANNs are very suitable for this application since they present classification success rates between 78% and 96% for all the situations examined. The method is based on Multilayer Perceptron Neural Network (MPNN) with sigmoid transfer function. The Levenberg-Marquard (LM) algorithm is used to adjust the parameters of MPNN. The required training data are obtained from transformer company.

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