Transfer Learning Methods for Magnetic Core Loss Modeling

Evan Dogariu, Haoran Li, Diego Serrano Lopez, Shukai Wang, Min Luo, Minjie Chen

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

2 Scopus citations

Abstract

Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point to initiate the model for another. This paper applies transfer learning to magnetic core loss modeling to reduce the amount of data needed to achieve improved performance for a variety of tasks. Leveraging a recently developed magnetic core loss dataset - MagNet - we demonstrate that a neural network trained for modeling the core losses of a certain group of magnetic materials under certain excitations can be retrained to model the core loss of other magnetic materials under similar excitations, with a reduced set of measurement data. This approach can also be applied to model the core loss of the same magnetic material under different excitations. Experiments are designed and compared to verify the effectiveness of material-to-material transfer learning and waveform-to-waveform transfer learning.

Original languageEnglish (US)
Title of host publication2021 IEEE 22nd Workshop on Control and Modelling of Power Electronics, COMPEL 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665436359
DOIs
StatePublished - 2021
Event22nd IEEE Workshop on Control and Modelling of Power Electronics, COMPEL 2021 - Cartagena, Colombia
Duration: Nov 2 2021Nov 5 2021

Publication series

Name2021 IEEE 22nd Workshop on Control and Modelling of Power Electronics, COMPEL 2021

Conference

Conference22nd IEEE Workshop on Control and Modelling of Power Electronics, COMPEL 2021
Country/TerritoryColombia
CityCartagena
Period11/2/2111/5/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Keywords

  • Core loss
  • Data-driven method
  • Machine learning
  • Neural network
  • Power magnetics
  • Transfer learning

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