With　the　exponential　growth　of　global　photovoltaic　(PV)　power　capacity,　it　is　essential　to　monitor,　detect　and　diagnose　the　faults　in　PV　arrays　for　optimal　operation.　This　paper　presents　an　improved　rotation　forest　(RoF)　algorithm　classifiers　ensemble　hybridized　with　extreme　learning　machine　(ELM)　for　fault　diagnosis　of　PV　arrays,　which　mainly　consists　of　feature　selection　and　classification.　In　the　feature　selection　step,　all　the　attributes　are　ranked　by　the　ReliefF　algorithm　and　the　top-ranked　attributes　are　chosen　to　create　the　new　training　data　subset.　In　the　classification　step,　the　base　classifier　decision　tree　of　the　RoF　is　replaced　by　the　extreme　learning　machine　to　form　a　new　hybrid　RoF-ELM　ensemble　classifier.　In　the　RoF-ELM　algorithm,　the　feature　space　is　first　split　into　several　subspaces　and　the　best　number　of　feature　subsets　is　found　through　the　traversal　search　method.　Then,　the　bootstrap　algorithm　is　employed　to　carry　out　bootstrap　resampling　for　each　feature　subspace,　and　the　principal　component　analysis　(PCA)　is　then　used　to　transform　the　resampled　samples.　Finally,　the　ELM　base　classifier　is　exploited　to　build　each　classification　model　and　the　fmal　decision　is　determined　by　the　simple　voting　approach.　By　combining　the　RoF　ensemble　method　with　the　ELM　classifier,　the　proposed　RoF-ELM　algorithm　not　only　overcomes　the　overfitting　problem　of　the　basic　RoF　algorithm,　but　also　improves　the　generalization　ability　of　the　basic　ELM.　In　order　to　experimentally　verify　the　proposed　approach,　different　types　and　levels　of　faults　have　been　created　in　a　laboratory　small　scale　grid-connected　PV　power　system　to　obtain　the　fault　data　samples.　Experimental　results　demonstrate　that　the　RoF-ELM　can　achieve　higher　diagnosis　accuracy　and　reliability　compared　to　the　basic　RoF　and　ELM　algorithms.　(C)　2019　The　Authors.　Published　by　Elsevier　Ltd.