In　recent　years,　the　methods　of　machine　learning　are　widely　investigated　to　resolve　the　series　arc　fault　(SAF)　diagnosis　problem　in　photovoltaic　(PV)　arrays.　However,　owing　to　the　factors　such　as　weak　signal　characteristics,　long　algorithm　execution　time,　and　sample　imbalance　in　practical　applications,　these　methods　may　have　diffi-culties　of　detecting　the　SAF.　To　address　these　problems,　a　method　based　on　the　Gramian　angular　summation　field　(GASF)　combined　with　the　squeeze　and　excitation-deep　convolution　generative　adversarial　network　(SE-DCGAN)　is　proposed.　Firstly,　the　absolute　difference　of　margin　factor　(ADMF)　of　the　current　signal　is　calculated　to　accurately　extract　the　transient　current　data　when　the　SAF　occurs.　Thereafter,　the　GASF　is　used　to　convert　transient　current　data　into　two-dimensional　images　to　amplify　the　universal　characteristics　of　the　SAF.　Subse-quently,　the　SE-DCGAN　is　adopted　to　augment　the　GASF　images　of　the　SAF　to　solve　the　problem　of　limited　SAF　samples.　Finally,　a　convolutional　neural　network　(CNN)　is　trained　to　identify　the　SAF.　Also,　a　fusion　sample　training　method　is　proposed　in　this　research,　that　is,　normal　samples　of　different　PV　systems　are　added　to　the　training　set　to　enhance　the　generalization　ability　of　CNN.　The　advantages　of　the　proposed　method　are　that　the　identification　of　SAF　is　improved　by　converting　one-dimensional　signals　into　two-dimensional　images,　and　the　generalization　ability　of　the　detection　model　is　improved　by　exploiting　the　common　features　of　SAFs　and　fusion　training.　The　validity　and　generalization　ability　of　the　proposed　method　are　verified　by　three　datasets　under　different　PV　systems.　Experimental　results　reveal　that　the　proposed　method　can　achieve　high　recognition　ac-curacy　for　the　measured　data;　moreover,　no　misjudgments　occurred　in　identifying　the　interference　events　such　as　maximum　power　point　tracking　(MPPT)　adjustment　and　irradiance　mutation　(IM).　In　addition,　the　experiments　confirm　that　the　fusion　training　method　enables　the　model　more　universal　and　applicable.