Fault　diagnosis　of　photovoltaic　(PV)　arrays　is　important　for　improving　the　reliability,　efficiency　and　safety　of　PV　power　stations,　because　the　PV　arrays　usually　operate　in　harsh　outdoor　environment　and　tend　to　suffer　various　faults.　Due　to　the　nonlinear　output　characteristics　and　varying　operating　environment　of　PV　arrays,　many　machine　learning　based　fault　diagnosis　methods　have　been　proposed.　However,　there　still　exist　some　issues:　fault　diagnosis　performance　is　still　limited　due　to　insufficient　monitored　information;　fault　diagnosis　models　are　not　efficient　to　be　trained　and　updated;　labeled　fault　data　samples　are　hard　to　obtain　by　field　experiments.　To　address　these　issues,　this　paper　makes　contribution　in　the　following　three　aspects:　(1)　based　on　the　key　points　and　model　parameters　extracted　from　monitored　I-V　characteristic　curves　and　environment　condition,　an　effective　and　efficient　feature　vector　of　seven　dimensions　is　proposed　as　the　input　of　the　fault　diagnosis　model;　(2)　the　emerging　kernel　based　extreme　learning　machine　(KELM),　which　features　extremely　fast　learning　speed　and　good　generalization　performance,　is　utilized　to　automatically　establish　the　fault　diagnosis　model.　Moreover,　the　Nelder-Mead　Simplex　(NMS)　optimization　method　is　employed　to　optimize　the　KELM　parameters　which　affect　the　classification　performance;　(3)　an　improved　accurate　Simulink　based　PV　modeling　approach　is　proposed　for　a　laboratory　PV　array　to　facilitate　the　fault　simulation　and　data　sample　acquisition.　Intensive　fault　experiments　are　carried　out　on　the　both　laboratory　PV　array　and　the　PV　model　to　acquire　abundant　simulated　and　experimental　fault　data　samples.　The　optimized　KELM　is　then　applied　to　train　the　fault　diagnosis　model　using　the　data　samples.　Both　the　simulation　and　experimental　results　show　that　the　optimized　KELM　based　fault　diagnosis　model　can　achieve　high　accuracy,　reliability,　and　good　generalization　performance.　©　2017　Elsevier　Ltd