Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning
نویسندگان
چکیده
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 PIML-based parameter estimation method demonstrated by case study of dc–dc Buck converter. A deep neural network the dynamic models converter are seamlessly coupled. It overcomes challenges related to training data, accuracy, robustness which typical data-driven approach has. This exemplary application envisions provide new perspective tailoring existing tools electronics.
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ژورنال
عنوان ژورنال: IEEE Transactions on Power Electronics
سال: 2022
ISSN: ['1941-0107', '0885-8993']
DOI: https://doi.org/10.1109/tpel.2022.3176468