Abstract: In order to alleviate the dependence of feature Self Extraction Model on voltage sag sample data and improve the ability of feature capture, a voltage sag event type identification algorithm based on improved auxiliary classifier generative adversarial networks (AC-GAN) was proposed in this paper. Firstly, the sag three-phase voltage data was transformed into a two-dimensional trajectory curve based on space phasor model (SPM), and the SPM trajectory image was used as the input of the intelligent model. The AC-GAN was improved by fusing the convolutional block attention module (CBAM) in the discriminator to improve the feature self extraction ability of the judgment model, so as to improve the performance of the whole AC-GAN network. In order to solve the problem of insufficient feature learning under unbalanced samples, the generated data, which are consistent with the real sample characteristics and distribution, were used to enhance the data. Finally, the real data scenarios in Jiangsu Province were used to verify the accuracy and stability of the proposed algorithm in identifying the type of sag under different data conditions.