On-Line Monitoring of Maximum Temperature and Loss Distribution of a Medium Frequency Transformer Using Artificial Neural Networks

Losses and maximum temperature are important indicators of the health status of the medium-frequency magnetic components. Both the losses and the maximum temperature are very difficult to obtain/estimate due to the high complexity, change of losses in the core with respect to the average temperature, or the variation of the losses of the windings derived from the construction of the litz wire and the high-frequency effects. This article proposes the use of a real-time estimator, based on artificial neural networks trained from finite element method simulations capable of predicting with an error less than 2% in all cases both the maximum internal temperatures and the losses of a medium frequency transformer, with forced convection, which implies a very complex thermal behavior.