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Keywords

inverter, fault diagnosis, neural network, deep cascade mode, fault characteristics

Abstract

Aiming at the open-circuit fault of the photovoltaic grid-connected three-phase voltage-type inverter,.a fault diagnosis method combining deep cascade mode-principal component analysis (DCM-PCA) and genetic algorithm-optimized BP(GA-BP) neural network is proposed. Firstly, the open-circuit fault of the inverter is analyzed and simulated, the three-phase current is determined as the fault signal, and 22 types of fault states are selected as the diagnosis objects, and the fault features are extracted through the deep cascade model with sparse representation classification as the basic operation unit, the DCM fault features are stratified based on the characteristics of hierarchical learning. The t-SNE method is used to verify that DCM has good feature extraction ability. PCA is used to reduce the redundancy of fault features, retain valuable principal components to improve the network mapping ability. Finally, the fault feature vector is used as the input of the GA-BP neural network to identify the fault and output the diagnosis result. The fault diagnosis accuracy of this method is 95.64% through simulation and experiments, compared with the DCM-PCA-BP, FFT-GA-BP and FFT-BP, the accuracy is increased by 8.71%, 20.64% and 51.70% respectively, indicating that the proposed method has better fault feature extraction capability and better fault diagnosis performance.

DOI

10.19781/j.issn.1673-9140.2024.01.027

First Page

260

Last Page

271

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