In　order　to　solve　the　problems　of　low　parameter　identification　accuracy　and　poor　stability　in　the　current　photovoltaic　module　model,　a　three-diode　photovoltaic　module　parameter　identification　model　(RLMRFO-TDM)　based　on　manta　ray　foraging　optimization　algorithm　and　the　refraction　learning　mechanism　is　proposed.　The　model　integrates　the　differential　evolution　mechanism　into　the　population　updating　link　of　MRFO　algorithm,　improves　the　local　exploration　ability　of　MRFO　algorithm　and　speeds　up　the　convergence　speed　of　MRFO　algorithm.　The　introduction　of　refraction　learning　mechanism　improves　the　randomness　of　MRFO　algorithm,　the　discreteness　of　population　in　the　search　area　and　the　global　search　ability　of　MRFO　algorithm.　The　benchmark　function　is　used　to　verify　the　effectiveness　of　RLMRFO　algorithm.　The　data　sets　of　STP6-120/36　and　STM6^0/36　photovoltaic　modules　are　used　to　test　the　performance　of　parameter　identification　of　RLMRFO-TDM　model.　Compared　with　other　models,　RLMRFO-TDM　model　has　the　best　identification　accuracy,　stability　and　convergence　speed.　©　2023　Chinese　Society　for　Measurement.　All　rights　reserved.