Automatic　fault　detection　and　diagnosis　techniques　for　photovoltaic　arrays　are　crucial　to　promote　the　efficiency,　reliability　and　safety　of　photovoltaic　systems.　In　recent　decades,　many　conventional　artificial　intelligence　approaches　have　been　successfully　applied　to　automatically　establish　fault　detection　and　diagnosis　model　using　fault　data　samples,　but　most　of　them　rely　on　manual　feature　extraction　or　expert　knowledge　to　build　diagnosis　models,　which　is　inefficient　and　may　ignore　some　potential　useful　features.　In　addition,　they　usually　use　shallow　neural　networks　with　limited　performance.　Addressing　the　issues,　this　paper　proposes　a　novel　intelligent　fault　detection　and　diagnosis　method　for　photovoltaic　arrays　based　on　a　newly　designed　deep　residual　network　model　trained　by　the　adaptive　moment　estimation　deep　learning　algorithm,　which　can　automatically　extract　features　from　raw　current-voltage　curves　and　ambient　irradiance　and　temperature,　and　effectively　improve　the　performance　with　a　deeper　network.　In　order　to　validate　the　proposed　fault　diagnosis　model,　a　Simulink　based　simulation　model　is　designed　for　a　real　laboratory　photovoltaic　array,　and　both　fault　simulation　and　real　experiments　are　carried　out　to　obtain　simulation　and　experimental　fault　datasets.　Furthermore,　two　other　popular　deep　learning　based　models　are　used　for　comparison,　including　convolution　neural　network　and　convolutional　auto-encoder.　Both　of　simulation　and　real　experimental　comparison　results　demonstrate　that　the　proposed　deep　residual　network　based　method　achieves　high　and　best　overall　performance　in　terms　of　accuracy,　generalization　performance,　reliability　and　training　efficiency.