Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning
Shuai Zhao, Yingzhou Peng, Yi Zhang, Huai Wang
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.
Topics & Concepts
ConvertersPower electronicsPower (physics)Electronic engineeringElectrical engineeringEstimationComputer scienceElectronicsControl engineeringPhysicsEngineeringSystems engineeringQuantum mechanicsModel Reduction and Neural NetworksSilicon Carbide Semiconductor TechnologiesMagnetic Properties and Applications