Rethinking Model-Based Fault Detection: Uncertainties, Risks, and Optimization Based on a Multilevel Converter Case Study

This article presents a probabilistic framework for assessing uncertainty and failure risk in model-based fault detection (MBFD) of power electronic systems. The proposed methodology encompasses uncertainty factor selection, uncertainty propagation, risk assessment, sensitivity analysis, and the development of tailored solutions to optimize MBFD performance. By quantifying two types of misdiagnosis, the risk-of-failure of MBFD has been evaluated under diversely random conditions. In a detailed case study on a modular multilevel converter (MMC), the framework has analyzed five different methods and revealed that existing MBFD methods can have misdiagnosis rates up to 20% due to uncertainties. By identifying leading uncertainty factors and mitigating their impacts, we have reduced the misdiagnosis rate to below 0.4%. While the MMC case study exemplifies practical implementation, the framework's generality makes it applicable to optimize fault detection across diverse power electronics applications.