Abstract
Physics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics, this article proposes a PIML-based parameter estimation method demonstrated by a case study of dc-dc Buck converter. A deep neural network and the dynamic models of the converter are seamlessly coupled. It overcomes the challenges related to training data, accuracy, and robustness which a typical data-driven approach has. This exemplary application envisions to provide a new perspective for tailoring existing machine learning tools for power electronics.
Original language | English |
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Journal | IEEE Transactions on Power Electronics |
Volume | 37 |
Issue number | 10 |
Pages (from-to) | 11567-11578 |
Number of pages | 12 |
ISSN | 0885-8993 |
DOIs | |
Publication status | Published - 1 Oct 2022 |
Bibliographical note
Publisher Copyright:© 1986-2012 IEEE.
Keywords
- Buck converter
- condition monitoring
- deep neural network
- physics-informed machine learning (PIML)
- prognostics and health management