Prediction of distribution transformer no-load losses using the learning vector quantization neural network

This paper presents an artificial neural network (ANN) approach to classification of distribution transformer no-load losses. The learning vector quantization (LVQ) neural network architecture is applied for this purpose. The ANN is trained to learn the relationship among data obtained from previous completed transformer constructions. For the creation of the training and testing set actual industrial measurements are used. Data comprise grain oriented steel electrical characteristics, cores constructional parameters, quality control measurements of cores production line and transformers assembly line measurements. 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