Online IGBT Short-Circuit Fault Diagnosis in Modular Multilevel Converter Based on DWT and RBFNN

Modular Multilevel Converters (MMCs) are widely adopted in high-power applications such as renewable energy integration and HVDC transmission, owing to their modularity and efficiency. Nevertheless, their complex architecture makes them susceptible to critical failures, particularly IGBT short-circuit faults, which can severely compromise system reliability. This paper presents a hybrid fault diagnosis approach that combines Discrete Wavelet Transform (DWT) and Radial Basis Function Neural Networks (RBFNN) for online detection and localization of IGBT short-circuit faults in MMCs. Capacitor voltage signals are decomposed using DWT, and the Root Mean Square (RMS) values of detail coefficients are extracted as features. These features are then processed by an RBFNN to accurately identify fault type and location. Simulation results demonstrate that the proposed method achieves fast, reliable, and accurate fault diagnosis, thereby enhancing the operational safety and stability of MMC-based systems.