Method for Accurately Predicting Core Losses Using Deep Learning

Abstract:
A new method for predicting ferrite properties, particularly core loss density, is proposed. Through the usage of this method, core loss in power transformers and inductors can be predicted with little use of measuring equipment, only in the training phase. Improvement on these predictions is done with the measurement at specific points with the help of deep learning methods. The architecture is described and the loss versus frequency, temperature, and peak magnetic field graphs from a ferrite material are processed. Finally, an inductor is built and measured, and its loss compared with the one predicted by the proposed method.