Fault Recognition of Multilevel Inverter Using Artificial Neural Network Approach

Abstract

This paper focuses on the development of a diagnostic tool for detecting insulated gate bipolar transistor power electronic switch flaws caused by both open and short circuit faults in multi-level inverter time-frequency output voltage specifications. High-resolution laboratory virtual instrument engineering workbench software testing tool with a sample rate data collection system, as well as specialized signal processing and soft computing technologies, are used in this proposed method. On a single-phase cascaded H-bridge multilevel inverter, simulation and experimental investigations of both open and short issues of the insulated gate bipolar transistor components are performed out. In all conceivable switch issues, the output voltage signals are evaluated for different modulation index values. Fast fourier transform and discrete wavelet transform methods are used to investigate the frequency domain properties of output voltage signals.In the artificial neural network, the back propagation training technique was employed, and the generated neural parameter values were used in the Laboratory Virtual Instrumentation Engineering Workbench real-time fault diagnosis model.