Abstract: Aiming at the problem of model overfitting caused by insufficient training samples in the automatic identification process of electroluminescence (EL) defects for half-cut photovoltaic modules, the deep convolutional generative adversarial networks (DCGANs) were adopted to generate the half-cut photovoltaic module EL images with controllable attributes. The similarity between the generated EL images and the captured EL images was evaluated by using the multi-scale structural similarity (MS-SSIM) index. The evaluation results show that the MS-SSIM indexes of all types of EL images generated by DCGANs and captured EL images are greater than 0.55, and most of the MS-SSIM values are near 0.7. In the training process of the classification models, the accuracy of the test set increases with the increase of the number of images generated in the training set. When the number of generated images reaches 6 000, the accuracy of the test set reaches 97.92%. The experimental results show that the DCGANs can generate half-cut photovoltaic module EL images with high quality and controllable attributes, which can better solve the problem of model overfitting caused by the lack of training samples.