Neural Network-Based Open Circuit Fault Detection and Diagnosis in Dtc-Svm Induction Motor Drives with Inverter Reconfiguration

Abstract

 In the modern era, managing and operating induction motors and inverter drives can be challenging when they encounter issues. Therefore, it's crucial to carefully consider these electrical systems to establish a dependable diagnostic of their components. Identifying strategies that allow us to supervise their operation and implement preventative measures to reduce frequent failures will significantly rely on early detection of faults. This study presents a pattern recognition approach for detecting open-circuit switch failures in inverters through the use of neural network. Additionally, we endeavored to reconfigure the inverter system to avert the occurrence of faults. The simulation findings and classification outcomes demonstrated improvements through the application of neural network for fault detection, identification, and reconfiguration.