Fault Diagnosis and Reconfiguration for Multilevel Inverter Drive Using AI-Based Techniques

Abstract

A fault diagnostic and reconfiguration method for a cascaded H-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques is proposed in this paper. Output phase voltages of the MLID are used as diagnostic signals to detect faults and their locations. It is difficult to diagnose an 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 an MLID system. Multilayer perceptron networks are used to identify the type and location of occurring faults. The principal component analysis 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 an NN, and the reduced noise can improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The proposed network is evaluated with simulation test set and experimental test set. The overall classification performance of the proposed network is more than 95%. A reconfiguration technique is also proposed. The proposed fault diagnostic system requires about six cycles to clear an open-circuit or short-circuit fault. The experimental results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration.