Open-circuit Fault Diagnosis for Grid-connected NPC Inverter based on Independent Component Analysis and Neural Network

Abstract

This paper is under in-depth investigation due to suspicion of possible plagiarism on a high similarity index

An open circuit (O-C) fault detection method for grid-connected neutral-point-clamped (NPC) inverter based on independent component analysis (ICA) and neural network (NN) is proposed in this paper. A NN classifier is applied to the fault diagnosis of NPC inverter. The ICA is utilized for the three phase current feature extraction. The ICA reduces the number of NN input neuron. A lower dimensional input space reduces the noise and the training time of NN, the ICA algorithm improves the mapping performance. The proposed algorithm is evaluated with simulation test set. The overall classification performance of the proposed network is more than 97%. The simulation results show that the proposed algorithm performs satisfactorily to fault location.