Fault Diagnosis of Multilevel Cascaded Inverter Using Multi Layer Perceptron Network

In this study, a fault diagnostic system in a multi-level inverter using a MLP network is developed. Using a mathematical model, it is difficult to diagnose a Multilevel-Inverter Drive (MLID) system, because MLID system complexity has a non-linear factor and it consist of many switching devices. Therefore neural network classification is applied to fault diagnosis of MLID system. Multilayer perceptron networks (MLP) are used to identify the type and location of occurring faults from inverter output voltage measurement. Here, MLP network based fault identification system for five level cascade H-bridge Multilevel Inverter (MLI) is analyzed. The proposed system identifies the fault with a greater accuracy and the results to various input patterns are presented for easy comprehension.