Electroluminescence　(EL)　imaging　is　an　effective　way　for　the　examining　of　photovoltaic　(PV)　modules.　Compared　with　manual　analysis,　using　Convolutional　Neural　Network　(CNN)　for　classification　is　much　more　convenient　but　it　requires　a　certain　amount　of　annotated　training　samples　which　cannot　be　acquired　handily.　In　this　paper,　we　present　a　method　for　augmenting　the　existing　dataset　of　EL　images　using　Generative　Adversarial　Networks　(GANS)　and　propose　a　model　called　AC-PG　GAN　aiming　at　this.　Three　chosen　CNN　models　are　used　to　examine　the　effectiveness　of　the　proposed　GAN　model　and　have　achieved　an　improvement　of　the　classification　accuracy　with　the　augmented　dataset　after　some　adjustment　and　the　maximum　improvement　is　up　to　14%.