As　photovoltaic　(PV)　arrays　are　exposed　to　the　outdoors　year-round,　they　are　susceptible　to　various　faults.　The　shading　condition,　degradation　or　dust　coverage　can　make　fault　signals　more　complex,　forming　compound　faults.　These　faults　can　lead　to　a　large　loss　of　power　generation　or　irreversible　damage　to　the　PV　modules,　and　even　fires　in　severe　cases.　Moreover,　unknown　fault　types　that　have　never　been　seen　in　the　training　set　may　occur　at　actual　working　conditions.　Therefore,　accurate　diagnosis　of　various　types　of　single　and　compound　faults　(closed-set　faults)　by　considering　the　identification　of　unknown　faults,　namely　open-set　faults　diagnosis,　is　crucial　to　improve　the　efficiency　of　operation　and　maintenance.　A　1D　VoVNet-SVDD　based　open-set　fault　diagnosis　model　for　PV　arrays　is　proposed.　The　model　is　a　two-stage　network　model　consisting　of　a　1D　VoVNet　network　and　a　multi-classification　Support　Vector　Data　Description　(SVDD)　in　series.　The　1D　VoVNet　network　automatically　extracts　fault　features　from　the　input　original　I-V　curve　data.　These　extracted　fault　features　are　then　combined　with　environmental　parameters　to　construct　the　SVDD　model.　The　SVDD　identifies　known　fault　types　by　con-structing　a　hypersphere　for　each　fault　type.　Fault　types　that　are　not　classified　into　any　of　the　hyperspheres　are　considered　as　unknown　faults,　enabling　open-set　diagnosis.　The　experimental　results　show　that　the　proposed　model　can　accurately　classify　the　closed-set　faults　among　the　three　designed　testing　tasks　while　identify　unknown　type　faults.　The　comparison　demonstrates　that　the　proposed　algorithm　is　superior　to　the　compared　models.