Design of Advanced Fault-Tolerant Control System for Three-Phase Matrix Converter Using Artificial Neural Networks

The Matrix converters (MCs) are widely used in a large number of applications such as Aircraft, Submarines, and AC drives. In these applications, because of the many chances of occurrence of a critical fault that leads to a halt of the system, and due to the failure of power electric switches, the MC operations can be compromised and there is a fear of burning of the components caused by the whole system shutdown. Therefore, a Fault-Tolerant Control System (FTCS) is extremely necessary for the continued operations of the Three-Phase Matrix Converter (TPMC) to improve the reliability and productivity of the system under faulty conditions. In this paper, an advanced FTCS is proposed based on detection, and dual hardware redundancy for TPMC. Two types of faults are injected into the system to observe the performance of the proposed system: internal open circuit faults on switches and external short circuit faults at the load side. A Fault Detection and Isolation (FDI) unit is used to detect and isolate the faulty switches using Artificial Neural Networks (ANN), and dual hardware redundancy in the switches has been proposed for fault tolerance. In case of external fault, a load-side fault detector is implemented using ANN. The simulation results in MATLAB/Simulink environment show the accurate and stable working of TPMC under faulty conditions, and hardware-in-the-loop is implemented with STM32-Nucleo-F103RB board to verify open circuit fault results. The proposed dual redundant FTC with FDI unit offers an excellent solution for the continued performance of TPMC which ultimately enhances the reliability of the system.