An intelligent technique for fault detection and localization of three-level ANPC inverter with NP connection for electric vehicles

Abstract

An intelligent technique for detecting and localizing an inverter switch fault or phase fault of a Three-Level Active Neutral Point Clamped (ANPC) inverter is proposed in this research. Moreover, a 3L-ANPC inverter can gain the controllability of EV's power train and not need to be stalled even after the occurrence of the fault. Hence, an efficient fault diagnosis methodology is required to identify the type of phase fault by a Support Vector Machine (SVM), a machine learning model consisting of sets of labeled training data with regression and classification challenges. Finally, when the fault occurs, the location of the switch fault can be identified by a Deep Neural Network (DNN), which consists of layers of neurons between the input and output layers which fuses the feature extraction process with increased accuracy. Thus, the detection and localization of the open-circuit fault of the switches in the ANPC inverter help overcome all single faults, hence gaining its current controllability without stopping the vehicle. The accuracy of fault detection is improved in a precise manner. Finally, the performance of the proposed work is evaluated over other conventional models concerning varied metrics like the accuracy of identification and localization.