arXiv:2609.00218v2 Announce Type: replace Abstract: Changes in circuit parameters can indicate component degradation in power converters, but measurement noise and limited parameter observability can make these changes difficult to detect reliably. This paper proposes an uncertainty-aware parameter estimation framework to distinguish genuine parameter changes from variations caused by estimation uncertainty. Using a differentiable switched converter model, the method requires only one or two samples per switching period. Model-derived residual whitening and a Bayesian posterior approximation quantify estimation uncertainty and reveal poorly identifiable parameters. Simulations demonstrate improved estimation accuracy over least-squares and physics-informed neural-network benchmarks, with uncertainty estimates consistent with repeated noise realizations. Experiments on a laboratory buck converter validate the estimated parameters against independently derived references and demonstrate the detection of a controlled output-capacitance reduction. The proposed framework provides a basis for reliable parameter-change detection in power converter condition monitoring.