Accurate　faults　diagnosis　for　photovoltaic　(PV)　array　is　one　of　the　vital　factors　that　guarantee　the　reliable　operation　of　PV　power　plant.　Artificial　intelligence　(AI)　based　fault　detection　and　diagnosis　(FDD)　models　are　promising　techniques.　In　order　to　automatically　extract　the　faults　features　from　the　raw　electrical　data　of　PV　array　and　create　efficient　FDD　model　with　small　dataset,　a　FDD　scheme　using　Wasserstein　generative　adversarial　network　(WGAN)　and　convolutional　neural　network　(CNN)　is　designed.　The　proposed　FDD　model　is　consisting　of　three　modules,　a　discriminator,　a　generator　and　a　classifier　for　fault　diagnosis.　By　analyzing　sequential　PV　data　in　a　2-Dimension　way,　the　proposed　discriminator　and　generator　learn　the　distribution　of　PV　data　under　various　PV　system　operations.　Then　they　are　utilized　to　generate　more　labeled　samples　to　improve　the　performance　of　the　CNN　based　classifier.　Thus,　the　proposed　FDD　model　can　be　trained　only　requiring　minor　labeled　samples.　A　laboratory　grid-connected　PV　system　is　established　to　experimentally　investigate　the　performance　of　the　developed　method.　The　results　demonstrate　that　the　designed　FDD　model　can　accurately　diagnose　line-line　and　open　circuit　faults.