How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics

Haoran Li, Diego Serrano, Thomas Guillod, Shukai Wang, Evan Dogariu, Andrew Nadler, Min Luo, Vineet Bansal, Niraj K. Jha, Yuxin Chen, Charles R. Sullivan, Minjie Chen

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This article applies machine learning to power magnetics modeling. We first introduce an open-source database - MagNet - which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions, consisting of more than 500 000 data points in its current state. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling the core losses and B-H loops. The neural network allows multiple factors that may influence the magnetic characteristics to be modeled in a unified framework, where the nonlinear behaviors are captured with high accuracy and high generality. Neural network models are found to be effective in compressing the measurement data and predicting the material characteristics, paving the way for 'neural networks as datasheets' to assist power magnetics design. Transfer learning is applied to the training of neural network models to further reduce the data size requirement while maintaining sufficient model accuracy.

Original languageEnglish (US)
Pages (from-to)15829-15853
Number of pages25
JournalIEEE Transactions on Power Electronics
Volume38
Issue number12
DOIs
StatePublished - Dec 1 2023

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • Core loss
  • data-driven method
  • hysteresis loop
  • machine learning
  • neural network
  • open-source database
  • power magnetics

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