In　order　to　accurately　and　efficiently　evaluate　the　actual　operating　status　of　photovoltaic　(PV)　power　stations,　this　study　proposes　a　novel　design　of　quick　current–voltage　(I-V)　curve　tracer　with　automatic　parameter　extraction　for　PV　arrays.　The　I-V　tracer　exploits　the　dynamic　capacitor　charging　to　quickly　and　safely　scan　the　output　I-V　curve　of　PV　array.　Particularly,　an　adaptive　sampling　interval　and　charging　and　discharging　time　estimation　method　is　proposed　to　achieve　the　uniform　sampling　of　the　I-V　curve　for　improving　the　data　quality.　Moreover,　an　efficient　grid　search　and　improved　Nelder-Mead　simplex　(GS-INMS)　based　hybrid　optimization　algorithm　is　proposed　for　PV　model　parameters　identification.　In　the　GS-INMS　algorithm,　the　grid　search　method　is　used　for　finding　starting　point　for　the　NMS　optimization　algorithm,　which　can　improve　the　global　search　capability.　Meanwhile,　the　basic　NMS　algorithm　is　modified　to　improve　the　local　convergence.　The　performance　of　the　proposed　algorithm　is　verified　and　compared　with　some　state-of-the-art　methods　on　several　experimental　datasets,　including　two　benchmark　I-V　curves,　large　dataset　of　I-V　curves　from　the　National　Renewable　Energy　Laboratory　(NREL),　and　the　I-V　curves　collected　by　the　designed　I-V　tracer　system.　Comprehensive　comparison　results　demonstrate　that　the　proposed　GS-INMS　shows　obvious　superiority　in　accuracy,　convergence　and　robustness.　Meanwhile,　because　of　low　complexity,　this　algorithm　is　embedded　in　the　designed　tracer　for　real-time　parameters　extraction　of　the　single-diode　model　of　PV　arrays.　In　addition,　the　experimental　results　of　field　testing　on　a　small-scale　PV　array　indicate　that　the　I-V　tracer　can　quickly　acquire　I-V　curves　of　high　quality　and　perform　accurate　model　parameter　identification　in　real　time.　©　2020　Elsevier　Ltd