Abstract
Three-phase inverters are widely used in electric power systems and their effective open-circuit fault diagnosis technique provides multiple benefits such as improved security and reliability, reduced cost, and maintenance time. In this paper, to improve the switch open-circuit fault diagnosis accuracy for a three-phase inverter, a novel optimization deep belief network (DBN) combined with particle swarm optimization (PSO) is presented. First, the DBN architecture is fully explained and a four-layer DBN including two hidden layers with a BP network is built. Then, the PSO is used to optimize the number of neurons in the hidden layers to increase the classification rate. Finally, all switch open-circuit fault diagnosis in a three-phase inverter based on the PSO-DBN is simulated and the results verified the effectiveness of the presented method. Compared with the DBN method, the PSO-DBN method has higher diagnostic accuracy.
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