Method for Accurately Predicting Core Losses Using Deep Learning

Conference: PCIM Europe digital days 2020 - International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management
07/07/2020 - 07/08/2020 at Deutschland

Proceedings: PCIM Europe digital days 2020

Pages: 7Language: englishTyp: PDF

Carmona, Miguel Angel; Gallego, Juan; Martinez, Alfonso (Frenetic, Spain)

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.