Fault Detection of NPC Inverter Based on Ensemble Machine Learning Methods

Three-level neutral point clamped (NPC) inverters have been widely adopted in different appliances, but their growing use leads to increased susceptibility to faults in the system. It is therefore essential to design precise and efficient methods that can detect inverter faults to ensure optimal control and prevent serious damage to the system. However, the most accurate fault diagnosis methods often require significant amounts of time to collect input data such as current and voltage images, or they involve lengthy data rows that are not commonly applicable to real-time applications. To compensate for these drawbacks, ensemble machine learning (EML) methods are proposed to detect open-circuit faults that only require one single point as an input. Moreover, the proposed methods were trained using DC-link voltage difference, time, and three phase currents to improve the accuracy of open-circuit fault detection. The feasibility and effectiveness of the proposed method are verified through simulation and experimentation. The present work also presents a comprehensive comparison of EML methods. The results show that Random Forest (RF) and Bootstrap Aggregating (bagging) methods achieve high performance compared to other EML methods, with an accuracy of 97%, without requiring additional circuitry. Additionally, the results show that incorporating time and DC-link voltage differences, along with three-phase current, improves the performance of EML methods.