Intelligent Fault Localization of Switches in Multilevel Inverter

Multilevel inverters (MLIs) based on IGBT switches have gained prominence in AC power applications due to their capability to reduce harmonic distortion while offering cost‐effective operation. They are widely adopted in power electronic systems; however, under high‐stress conditions, power switches are prone to faults that can impair system performance. Hence, effective identification of faulty switches is crucial. This study focuses on detecting single and multiple switch open‐circuit faults (OCFs) in reduced device count (RDC) MLI. A machine learning (ML)–based diagnostic framework is proposed, which utilizes only the output voltage signals for fault analysis. From these signals, three key features are extracted: standard deviation, half‐cycle moving average, and total harmonic distortion for fault classification. Several ML classifiers are evaluated and benchmarked against recent approaches, with the decision tree (DT) model achieving the highest accuracy of 99.84% under a 70:30 training‐to‐testing split. The proposed method accurately identified both single and multiple switch OCFs in RDC‐MLI within 10–30 ms. The complete diagnostic system is implemented and validated in the MATLAB/Simulink environment.