Cascaded H-bridge multilevel inverter drives operating under faulty condition with AI-Based fault diagnosis and reconfiguration

The ability of cascaded H-bridge multilevel inverter drives (MLED) to operate under faulty condition including AI-based fault diagnosis and reconfiguration system is proposed in this paper. Output phase voltages of a MLED can be used as valuable information to diagnose faults and their locations. It is difficult to diagnose a MUD 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 MLED system. Multilayer perceptron (MLP) 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 genetic algorithm (GA) is also applied to select the valuable principal components to train the NN. A reconfiguration technique is also proposed. The proposed system is validated with simulation and experimental results. The proposed fault diagnostic system requires about 6 cycles (similar to 100 ms at 60 Hz) to clear an open circuit and about 9 cycles (similar to 150 ins at 60 Hz) to clear a short circuit fault. The experiment and simulation results are in good agreement with each other, and the results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration.