Parameter Estimation of Power Electronic Converters with Physics-Informed Machine Learning

Shuai Zhao, Yingzhou Peng, Yi Zhang, Huai Wang

    Research output: Contribution to journalArticlepeer-review

    18 Scopus citations

    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 languageBritish English
    Pages (from-to)11567-11578
    Number of pages12
    JournalIEEE Transactions on Power Electronics
    Volume37
    Issue number10
    DOIs
    StatePublished - 1 Oct 2022

    Keywords

    • Buck converter
    • condition monitoring
    • deep neural network
    • physics-informed machine learning (PIML)
    • prognostics and health management

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