Lightweight shuffle–SimAM network-based open-circuit fault diagnosis of grid-connected cascaded H-bridge inverters

Abstract

Aiming at the problems of high similarity and difficulty in extracting the fault features of power-switching tubes, as well as the high complexity of fault diagnosis models, the large number of parameters, and the long fault diagnosis time of the multilevel cascaded H-bridge inverter in medium-voltage and high-voltage applications, this study proposes a fault diagnosis method based on a lightweight shuffle–SimAM network. First, the proposed method establishes a lightweight parallel ShuffleNet network model and utilizes multi-sensor data as the input of each parallel network for the initial extraction of similar fault features. Second, a feature fusion module is constructed inside the network to weight and fuse the features extracted at each level of the parallel network. Then the fused features are successively advanced to further enhance the extraction of similar fault features. Finally, to maintain the network with high diagnostic accuracy while improving the level of lightweighting, deep separable convolution and SimAM parameter-free attention mechanisms are introduced into the diagnostic network. Experimental results show that the proposed method effectively reduced the complexity of the model and the diagnosis time while maintaining a high diagnosis accuracy.