The　vast　majority　of　photo　voltaic　(PV)　arrays　often　work　in　harsh　outdoor　environment,　and　undergo　various　fault,　such　as　local　material　aging,　shading,　open　circuit,　short　circuit　and　so　on.　The　generation　of　these　fault　will　reduce　the　power　generation　efficiency,　and　even　lead　to　fire　disaster　which　threaten　the　safety　of　social　property.　In　this　paper,　an　on-line　distributed　monitoring　system　based　on　ZigBee　wireless　sensors　network　is　designed　to　monitor　the　output　current,　voltage　and　irradiate　of　each　PV　module,　and　the　temperature　and　the　irradiate　of　the　environment.　A　simulation　PV　module　model　is　established,　based　on　which　some　common　faults　are　simulated　and　fault　training　samples　are　obtained.　Finally,　a　genetic　algorithm　optimized　Back　Propagation　(BP)　neural　network　fault　diagnosis　model　is　built　and　trained　by　the　fault　samples　data.　Experiment　result　shows　that　the　system　can　detect　the　common　faults　of　PV　array　with　high　accuracy.　©　Springer　International　Publishing　Switzerland　2015.