The　effective　fault　diagnosis　algorithm　for　the　DC　side　photovoltaic　(PV)　array　of　a　PV　system　(PVS)　plays　an　important　role　in　the　operation　efficiency　and　safety　for　PV　power　plants.　But　for　fault　diagnosis　models　it　may　fail　to　diagnose　PV　array　(PVA)　faults　without　detailed　and　quite　fine　fault　features,　especially　line-line　faults　(LLF)　occurring　in　the　PVS　that　works　under　complex　working　conditions　like　low　irradiance　conditions　and　LLF　with　fault　impedance.　To　address　these　challenges,　this　paper　proposes　a　fault　diagnosis　scheme　to　diagnose　different　PVA　faults　using　a　proposed　Dual-channel　Convolutional　Neural　Network　(DcCNN),　which　is　able　to　automatically　extract　features　and　weight　these　features　for　fault　classification.　The　important　and　fine　features　from　the　current　and　voltage　electrical　time　series　graph　(ETSG)　are　extracted　respectively　by　DcCNN　in　a　double　input　way.　Then,　a　proposed　feature　selection　structure　(FSS)　is　designed　to　improve　the　proposed　fault　diagnosis　model　capacity　for　diagnosing　PVA　faults　under　various　conditions,　including　LLF,　partial　shading　condition　(PSC)　and　open　circuit　faults　(OCF).　Comparing　to　manually　designed　features,　FSS　not　only　helps　DcCNN　extract　important　features　from　PVA　current　and　voltage　automatically　but　also　evaluates　extracted　features　for　further　classification　of　DcCNN.　Moreover,　in　the　training　stage,　a　proposed　penalty　is　applied　on　DcCNN　to　constrain　FSS,　resulting　in　its　sparse　weight　distribution.　A　comprehensive　experiment　based　on　a　laboratory　roof　grid　connected　PVS　is　conducted.　The　results　demonstrate　the　superior　performance　of　the　proposed　approach　compared　with　other　algorithms　as　it　can　extract　high-discriminative　features　from　PVA　current　and　voltage　for　different　PVA　faults,　which　is　also　effective　on　diagnosing　LLF　under　low　irradiance　conditions　and　LLF　with　fault　impedance.