Multi-Material Power Magnetics Modeling with a Modular and Scalable Machine Learning Framework

Edward Deleu, Haoran Li, Joe Li, Wonju Lee, Thomas Guillod, Charles R. Sullivan, Shukai Wang, Minjie Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper presents a modular and scalable machine learning framework for multi-material magnetic core loss modeling. The neural network framework is trained to predict core loss based on a flux density excitation waveform B(t) as well as additional scalar inputs including temperature, frequency, and dc-bias in order to handle a wide range of operating conditions. The framework is implemented such that a large portion of the model, the feature extractor, is shared for multiple materials, while specific materials require very few parameters in individual feature mapping networks. This allows the framework to 1) effectively model various materials with a scalable neural network structure and low parameter count; 2) accurately predict core losses across a wide operation range; and 3) adaptively support new materials with additional material-specific mapping networks trained with limited new data.

Original languageEnglish (US)
Title of host publication2024 IEEE Applied Power Electronics Conference and Exposition, APEC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages370-377
Number of pages8
ISBN (Electronic)9798350316643
DOIs
StatePublished - 2024
Event39th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2024 - Long Beach, United States
Duration: Feb 25 2024Feb 29 2024

Publication series

NameConference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC
ISSN (Print)1048-2334

Conference

Conference39th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2024
Country/TerritoryUnited States
CityLong Beach
Period2/25/242/29/24

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • core loss
  • machine learning
  • neural networks
  • power magnetics
  • soft magnetic materials
  • transformer model

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