This repository includes the code and data for the IEEE TPEL paper "Parameter Estimation of Power Electronic Converters with Physics-informed Machine Learning"
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 paper 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.
@article{Zhao2022_IEEETPEL,
title={Parameter Estimation of Power Electronic Converters with Physics-informed Machine Learning},
author={Zhao, Shuai and Peng, Yingzhou and Zhang, Yi and Wang, Huai},
journal={IEEE Trans. Power Electron.},
year={Oct. 2022},
volume={37},
number={10},
pages={11567-11578},
doi={10.1109/TPEL.2022.3176468}
}