Recently,　promising　progresses　have　been　made　in　photovoltaic　(PV)　arrays　fault　diagnosis　(FD)　due　to　the　importance　of　operation　and　maintenance　of　PV　power　plants.　However,　PV　arrays　inevitably　experience　gradual　degradation　due　to　the　complexity　of　operating　conditions,　resulting　in　domain　shift　of　output　data,　which　has　a　significant　negative　impact　on　the　performance　of　FD.　To　address　these　problems,　this　study　proposes　a　two-stage　cross-domain,　i.e.,　adaptive　generative　adversarial　network　deep　learning　approach　for　PV　arrays　FD　under　different　degradation　levels.　In　the　first　stage,　the　Normal　data　from　the　source　domain　(PV　arrays　without　performance　degradation)　is　utilized　for　training.　Then,　the　Maximum　Mean　Discrepancy　(MMD)　loss　is　introduced　to　the　fault　generators　in　adversarial　training　to　produce　high-level　feature　representations　of　source　domain　fault　data.　In　the　second　stage,　identical　training　steps　are　used　to　guide　the　fault　generators.　Specifically,　Normal　data　from　the　target　domain　i.e.,　PV　arrays　with　performance　degradation,　is　utilized　to　generate　fault　data　features　that　are　consistent　with　the　target　domain　features.　Then,　the　cross-domain　adaptive　FD　model　can　be　trained　by　using　generated　fault　data　features.　The　proposed　model　can　not　only　learn　the　relationship　from　the　different　types　of　data,　but　also　utilize　target　domain　PV　array　data　under　healthy　conditions　to　manually　generate　fake　samples　for　cross-domain　adaptive　FD.　Experimental　results　show　that　the　Precision　of　the　proposed　model　in　the　two　tasks　is　98.34　%　and　92.93　%,　with　Recall　is　98.23　%　and　94.13　%,　F1-Score　is　0.9823　and　0.9274,　all　of　which　are　better　than　those　of　the　comparison　models.　©　2025　Elsevier　Ltd