Physics-informed Machine Learning for Parameter Estimation of DC-DC Converter

Shuai Zhao, Yingzhou Peng, Yi Zhang, Huai Wang

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    Although various machine learning-based methods have been proposed for condition monitoring in power elec-tronics, they are challenging to be implemented in practice due to the accuracy, data availability, computation burden, explainability, etc. Physics-informed machine learning (PIML) has been emerging as a promising direction where the above challenges can be mitigated by incorporating domain knowledge. In this paper, we propose a PIML- based parameter estimation method for a DC-DC Buck converter, as an exemplary application of PIML in power electronics. By seamlessly integrating a deep neural network and the converter physical model, it can estimate multiple component parameters simultaneously with high accuracy and robustness, while based on a limited dataset. It expects to provide a new perspective to tailor existing ML tools for power electronic applications.

    Original languageBritish English
    Pages324-329
    Number of pages6
    DOIs
    StatePublished - 2022
    Event37th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2022 - Houston, United States
    Duration: 20 Mar 202224 Mar 2022

    Conference

    Conference37th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2022
    Country/TerritoryUnited States
    CityHouston
    Period20/03/2224/03/22

    Fingerprint

    Dive into the research topics of 'Physics-informed Machine Learning for Parameter Estimation of DC-DC Converter'. Together they form a unique fingerprint.

    Cite this