Transformer Fault Diagnosis Based on WGAN-GP and Efficient Convolutional Block Attention Mechanism IPOA-ICNN
Aiming at the problems of unbalanced data and insufficient feature information extraction in fault samples collected by transformer fault diagnosis,a data-enhanced and efficient convolutional block attention module(ECBAM)is proposed to optimize one-dimensional improved convolutional neural network(1D-ICNN)for the transformer fault diag-nosis.Firstly,a Wasserstein generative adversarial network with gradient penalty(WGAN-GP)is established to train the unbalanced transformer data samples,and synthetic samples are generated for data enhancement.The gas characteristic parameters with strong correlation are selected by variance analysis.Secondly,the residual and efficient convolutional block attention mechanism modules are used to extract more detailed features from the reconstructed balanced samples to realize the classification of fault diagnosis networks.The improved pelican optimization algorithm(IPOA)is used to opti-mize the ICNN parameters.The comparison and analysis of examples show that the proposed algorithm has higher accu-racy and stability in fault diagnosis performance,and the effectiveness of the fault diagnosis classification performance of the proposed model is verified.
万方数据
维普
