A Reconfiguration Technique for Multilevel Inverters Incorporating Diagnostic System Based on Neural Network

A reconfiguration technique for multilevel inverters incorporating a diagnostic system based on neural network is proposed in this paper. It is difficult to diagnose a multilevel-inverter drive (MLID) system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron networks are used to identify the type and location of occurring faults. The principal component analysis (PCA) is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train a NN, and the reduced noise may improve the mapping performance. The output phase voltage of a MLID can be used to diagnose the faults and their locations. The reconfiguration technique is also proposed. The effects of using the proposed reconfiguration technique at high modulation index are addressed. The proposed system is validated with experimental results. The experimental results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration