Investigation analysis of open circuit and short circuit fault on cascaded H-bridged multilevel inverter using artificial neural network approach

Abstract

Cascaded H-bridge multilevel inverters are becoming increasingly used in applications such as distribution systems, electrical traction systems, high voltage direct conversion systems, and many others. Despite the fact that multilevel inverters contain a large number of control switches, detecting a malfunction takes a significant amount of time. In the fault switch configurations diode included for freewheeling operation during open-fault condition. During short circuit fault conditions are carried out by the fuse, which can reveal the freewheeling current direction. The fault category can be identified independently and also failure of power switches harmed by the functioning and reliability of cascaded H-bridge multilevel inverters. This paper investigates the effects and performance of open and short switching faults of multilevel inverters. Output voltage characteristics of five level MLI are frequently determined from distinctive switch faults with modulation index value of 0.85 is used during simulation analysis. In the simulation study, with the modulation index value of 0.85, one second open and short circuit faults are generated for the location of the defective switch. Fault is identified automatically by means of artificial neural network technique using sinusoidal pulse width modulation based on distorted total harmonic distortion and managed by its own. The proposed topology is to be design and evaluate using MATLAB/Simulink platform.