发表机构
Aalborg University（奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于贝叶斯最大后验优化与可微变换器模型的不确定性感知参数估计框架，经合成数据与硬件Buck变换器验证，可准确估计可辨识参数并揭示部分参数的弱实际可辨识性。
AI 中文摘要
参数估计广泛应用于电力变换器的状态监测，但现有多数方法仅提供点估计，无法量化观测到的参数变化是否具有统计显著性。本文提出一种基于贝叶斯最大后验优化与可微变换器模型的不确定性感知参数估计框架，采用拉普拉斯近似获取局部高斯后验分布，实现不确定性量化、一致性检验、估计器分辨率分析及跨数据窗口的精度加权池化。该方法在合成数据与硬件Buck变换器上验证，可准确估计可辨识参数，并揭示在现有传感配置下MOSFET导通电阻等参数的弱实际可辨识性。
英文摘要
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.
