Fault Diagnosis System for a Multilevel Inverter Using a Principal Component Neural Network

A fault diagnosis system in a multilevel-inverter using a compact 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 classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults from inverter output voltage measurement. The neural network design process is clearly described. The principal component analysis (PCA) is utilized to reduce the neural network input size. A lower dimensional input space will also usually reduce the time necessary to train a neural network, and the reduced noise may improve the mapping performance. The comparison between MLP neural network (NN) and PC neural network (PC-NN) are performed. Both proposed networks are evaluated with simulation test set and experimental test set. The PC-NN has improved overall classification performance from NN by about 5% points. The overall classification performance of the proposed networks is more than 90%. Thus, by utilizing the proposed neural network fault diagnosis system, a better understanding about fault behaviors, diagnostics, and detections of a multilevel inverter drive system can be accomplished. The results of this analysis are identified in percentage tabular form of faults and switch locations.