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A fault diagnosis system in a multilevel-inverter using a compact PSO and neural network is proposed in this paper. It is difficult to diagnosis 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 fault from inverter output voltage measurement. Particle Swarm Optimization (PSO) technique 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.
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