Fault　diagnosis　of　photovoltaic　array　plays　an　important　role　in　operation　and　maintenance　of　PV　power　plant.　The　nonlinear　characteristics　of　photovoltaic　array　and　the　Maximum　Power　Point　Tracking　technology　in　the　inverter　prevent　conventional　protection　devices　to　trip　under　certain　faults　which　reduces　the　system＇s　efficiency　and　increases　the　risks　of　fire　hazards.　In　order　to　better　diagnose　photovoltaic　array　faults　under　Maximum　Power　Point　Tracking　conditions,　the　sequential　data　of　transient　in　time　domain　under　faults　are　analyzed　and　then　applied　as　the　input　fault　features　in　this　work.　Firstly,　the　sequential　current　and　voltage　of　the　photovoltaic　array　are　transformed　into　a　2-Dimension　electrical　time　series　graph　to　visually　represent　the　characteristics　of　sequential　data.　Secondly,　a　Convolutional　Neural　Network　structure　comprising　nine　convolutional　layers,　nine　max-pooling　layers,　and　a　fully　connected　layer　is　proposed　for　the　photovoltaic　array　fault　diagnosis.　The　proposed　model　for　photovoltaic　array　fault　diagnosis　integrates　two　main　parts,　namely　the　feature　extraction　and　the　classification.　Thirdly,　this　model　automatically　extracts　suitable　features　representation　from　raw　electrical　time　series　graph,　which　eliminates　the　need　of　using　artificially　established　features　of　data　and　then　employs　for　photovoltaic　fault　diagnosis.　Moreover,　the　proposed　Convolutional　Neural　Network　based　photovoltaic　array　fault　diagnosis　method　only　takes　the　array　of　voltage　and　current　of　the　photovoltaic　array　as　the　input　features　and　the　reference　panels　used　for　normalization.　The　proposed　approach　of　photovoltaic　array　fault　diagnosis　achieved　over　99%　average　accuracy　when　applied　to　the　case　studies.　The　comparisons　of　the　experimental　results　demonstrate　that　the　proposed　method　is　both　effective　and　reliable.　©　2019　Elsevier　Ltd