A　new　online　intelligent　fault　diagnosis　method　is　proposed　for　PV　arrays　in　this　paper　to　improve　the　reliability　and　efficiency　of　PV　systems.　Firstly,　a　new　seven-dimensional　fault　feature　vector　is　extracted　from　the　raw　data　of　dynamic　operating　points　of　PV　arrays　including　operating　voltage,　current,　irradiance　and　temperature.　Secondly,　an　optimized　adaptive　network　based　fuzzy　inference　system　(ANFIS)　is　proposed　as　the　fault　diagnosis　model.　Lastly,　the　feasibility　and　superiority　of　the　proposed　ANFIS　based　fault　diagnosis　model　are　tested　by　both　Simulink　based　simulation　and　real　fault　experiments　on　a　laboratory　PV　system.　Experimental　results　validate　that　the　proposed　ANFIS　based　method　achieves　a　high　performance　and　is　superior　to　conventional　back-propagation　neural　network　(BPNN)　based　methods.　The　overall　accuracy　of　the　ANFIS　based　fault　diagnosis　model　on　the　simulation　and　experimental　dataset　is　99.9%　and　over　97%　respectively.　©　Published　under　licence　by　IOP　Publishing　Ltd.