Using supervised machine learning in power converters design for particle accelerators – Application to magnetic components design

Abstract

This paper presents an efficient application of Ma-chine Learning (ML) to derive models for accurately predicting the inductance value and mechanical constraints in widely used air-cored inductors in power electronics systems for accelerators. The ML is trained on Finite Elements Analyses (FEA) obtained data. The obtained Artificial Neural Network (ANN) based models are then used in a numerical optimization environment able to efficiently provide optimal solution in terms of speed and accuracy.