Fault Detection in NPC Multilevel Inverter Using Artificial Neural Network

The evolution of power conversion technologies, particularly multilevel inverters, plays a crucial role in the efficiency and sustainability of renewable energy systems and electric vehicle (EV) drive trains. Despite their advanced capabilities in managing high power levels, the complex nature of multilevel inverters makes them prone to faults like open circuit faults, short circuit faults, and other faults, challenging their operational reliability. Traditional fault detection methods limited in scope and precision, fail to address the critical needs of these systems effectively. In response, this paper proposes an innovative Artificial Neural Network (ANN)-based approach for rapid and accurate fault detection of power switches within multilevel inverters. The paper mainly focuses on inverter power switch faults such as open circuit and short circuit fault detection. Utilizing dynamic learning and the adaptive capabilities of ANN, our method outperforms conventional techniques by providing robust fault diagnosis through detailed MATLAB simulations under varied operational scenarios. This approach not only enhances fault detection efficiency but also significantly contributes to maintaining the integrity of power conversion systems, highlighting the potential of advanced computational techniques in resolving maintenance and operational challenges in complex power electronic systems within a concise framework.