Abnormal　conditions　of　field　photovoltaic　(PV)　array　such　as　open-circuit,　short-circuit,　and　partial　shading　are　embedded　in　DC　side　voltage/current　curves.　Besides,　meteorological　factors　as　solar　radiation,　wind　speed,　and　ambient　temperature　can　also　influence　fault　behaviors.　To　realize　abnormal　identify　of　PV　array　under　environmental　interference,　this　paper　presents　a　graph　attention　network　(GAT)　based　fault　detection　algorithm　for　PV　array.　Fault　simulation　experiments　are　conducted　on　grid-connected　PV　array,　and　the　voltage/current　curves　of　the　PV　DC　side　under　different　states　(normal,　open-circuit,　short-circuit,　and　partial　shading)　were　collected　to　build　dataset.　One-dimensional　convolution　and　two　parallel　graph　attention　layers　are　adopted　to　extract　temporal　and　dimensional　features　of　the　voltage/current　series.　A　gated　recurrent　unit　(GRU)　is　employed　to　capture　the　long-term　dependencies　of　the　time-series　data.　Fully　connected　(FC)　layers　and　variational　auto-encoder　(VAE)　are　combined　optimized　for　detecting　and　locating　the　PV　abnormal　events.　Model　performance　are　compared　with　Robust　Anomaly　Detection　(OmniAnomaly),　Transformer　Networks　for　Anomaly　Detection　(TranAD),　and　Long　Short-Term　Memory　(LSTM),　result　show　that　the　proposed　grid-connected　PV　array　fault　detection　model　achieves　an　accuracy　of　96.8%　on　the　test　dataset,　providing　an　effective　method　for　fault　diagnosis　of　grid-connected　PV　systems　under　different　meteorological　conditions.　©　Beijing　Paike　Culture　Commu.　Co.,　Ltd.　2024.