Uncertainty-Aware Parameter Estimation for Condition Monitoring of Power Converters

Abstract

Parameter estimation is widely used for condition monitoring of power converters, but most existing methods provide only point estimates and therefore cannot quantify whether an observed parameter change is statistically significant. This paper proposes an uncertainty-aware parameter estimation framework based on Bayesian maximum a posteriori optimization and a differentiable converter model. A Laplace approximation is used to obtain a local Gaussian posterior, enabling uncertainty quantification, consistency testing, estimator-resolution analysis, and precision-weighted pooling across data windows. The method is validated on synthetic and hardware Buck converter. It demonstrates accurate estimation of well-identified parameters and reveal the weak practical identifiability of parameters such as MOSFET on-resistance under the available sensing configuration.