Under　the　background　of　the　large-scale　construction　of　photovoltaic　(PV)　power　stations,　it　is　crucial　to　discover　and　solve　module　failures　in　time　for　improving　the　service　life　and　maintaining　the　normal　operation　efficiency　of　modules.　Based　on　analyzing　the　difference　of　I-V　curves　of　PV　arrays　under　different　fault　states,　the　I-V　curves,　temperatures　and　irradiances　are　taken　as　input　data,　and　a　fusion　model　of　convolutional　neural　network　(CNN)　and　residual-gated　recurrent　unit　(Res-GRU)　is　proposed　to　identify　the　PV　array　fault.　This　model　consists　of　a　1-dimensional　CNN　module　with　a　4-layer　structure　and　a　Res-GRU　module.　It　has　the　advantages　of　end-to-end　fault　diagnosis,　no　manual　feature　extraction,　strong　anti-interference　ability,　and　usable　in　the　absence　of　irradiances　and　temperatures.　Moreover,　it　can　not　only　identify　a　single　fault　(e.g.,　short　circuit,　partial　shading,　abnormal　aging,　etc.),　but　also　can　effectively　identify　hybrid　faults.　Experimental　results　show　that　the　classification　accuracy　of　the　proposed　method　is　98.61%,　which　is　better　than　the　ones　of　the　artificial　neural　network　(ANN),　the　extreme　learning　machine　with　kernel　function　(KELM),　the　fuzzy　C-mean　(FCM)　clustering,　the　residual　neural　network　(ResNet),　and　the　stage-wise　additive　modeling　using　multi-class　exponential　loss　function　based　on　the　classification　and　regression　tree　(SAMME-CART).　In　addition,　in　the　absence　of　temperatures　and　irradiances,　the　classification　accuracy　still　reaches　95.23%,　which　has　a　broad　application　prospect　in　the　online　fault　diagnoses　of　PV　arrays.　©　2013　IEEE.