Neural Network as Datasheet: Modeling B-H Loops of Power Magnetics with Sequence-to-Sequence LSTM Encoder-Decoder Architecture

Diego Serrano, Haoran Li, Thomas Guillod, Shukai Wang, Min Luo, Charles R. Sullivan, Minjie Chen

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

Abstract

This paper presents the concept of 'Neural Network as Datasheet' for B- H loop modeling of power magnetics with sequence-to-sequence machine learning. Instead of directly pre-senting the measured characteristics of magnetic core materials, we employ a neural network to capture the B-H loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures. The training and inference process of the neural network are fully automated to minimize the impact of human error. Neural networks are also effective in compressing the information contained in the raw database to avoid data search or interpolation. The neural network can be used to rapidly predict B- H loops under different operating conditions and support circuit simulations. Based on a recently developed large-scale magnetic core loss database - MagNet - we demonstrate that a neural network datasheet can effectively compress and release information about power magnetics and can play important roles in power electronics converter design.

Original languageEnglish (US)
Title of host publication2022 IEEE 23rd Workshop on Control and Modeling for Power Electronics, COMPEL 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665410816
DOIs
StatePublished - 2022
Event23rd IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2022 - Tel-Aviv, Israel
Duration: Jun 20 2022Jun 23 2022

Publication series

NameProceedings of the IEEE Workshop on Computers in Power Electronics, COMPEL
Volume2022-June
ISSN (Print)1093-5142

Conference

Conference23rd IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2022
Country/TerritoryIsrael
CityTel-Aviv
Period6/20/226/23/22

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • B-H loop
  • Core Loss
  • Ferrites
  • Magnetic Materials
  • Neural Networks

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