Uncertainty-Informed Threshold Assessment of Model-Based Fault Detection for Modular Multilevel Converters

作     者：Liao, Yantao Zhang, Yi You, Jun Jin, Long Xu, Zhike Shen, Zhan 

作者机构：Southeast Univ Dept Elect Engn Nanjing 210096 Peoples R China Aalborg Univ AAU Energy DK-9220 Aalborg Denmark 

出 版 物：《IEEE OPEN JOURNAL OF POWER ELECTRONICS》 (IEEE Open J. Power Electron.)

年 卷 期：2024年第5卷

页      面：802-811页

核心收录：

基　　金：National Natural Science Foundation of China 

主　　题：Uncertainty Fault detection Voltage measurement Probabilistic logic Power electronics Optimization Multilevel converters Model-based fault detection modular multilevel converters uncertainty quantification threshold assessment 

摘      要：Determining threshold values in model based fault detection (MBFD) is a longstanding challenge, which is often addressed through empirical and ambiguous adjustments. To tackle this issue, this paper proposes an uncertainty-informed framework for quantitative threshold assessment. The framework comprises three stages: 1) identifying uncertainties by explicitly understanding the implemented MBFD method, 2) quantifying fault detection residual through uncertainty propagation, and 3) determining and optimizing threshold values based on the quantified misdiagnosis rates. To validate the effectiveness of the proposed approach, a case study of a modular multilevel converter is selected. The proposed method not only enables a quantified threshold assessment but also enhances the robustness of the fault detection by accounting for uncertainties.