CNN-Based Robust Harmonic Feature Extraction for Open-Circuit Fault Detection and Localization in Modular Multilevel Converters

This letter presents a novel convolutional neural network (CNN)-based methodology for robust and accurate open-circuit fault detection and submodule (SM) localization in modular multilevel converters. Instead of an end-to-end classifier, the proposed method employs the CNN as a physics-informed feature extractor to enhance a foundational theoretical model by robustly estimating switching frequency harmonic components from arm voltage measurements. Crucially, the CNN effectively mitigates the detrimental impacts of measurement noise and sampling frequency variations. This method offers low sensor requirements, adaptability to diverse operating conditions, and high computational efficiency. Simulation results demonstrate a 61.3% overall performance improvement, showcasing enhanced detection speed, SM localization accuracy, and robustness compared to the theoretical model under practical constraints. Experimental validation on a laboratory prototype further substantiates these improvements, achieving fault detection and localization on average 15 ms and 22.5 ms faster than the baseline theoretical model respectively, showcasing its practical applicability.