The　smart　grid　is　rapidly　developing　to　become　highly　connected　and　automated.　These　advancements　have　been　mainly　attributed　to　the　ubiquitous　data　communication　in　the　grid.　However,　low　sampling　frequency　will　limit　the　utilization　degree　of　data　because　low　frequency　measurement　power　data　contains　little　information.　The　existing　methods　of　reconstructing　the　low-frequency　sampling　data　into　the　high-frequency　sampling　data　have　poor　accuracy　of　data　reconstruction　since　most　of　them　failed　to　capture　the　characteristics　of　power　data.　This　paper　proposes　a　novel　method　based　on　super-resolution　generative　adversarial　network　(SRGAN)　to　address　this　issue.　First,　we　convert　power　data　into　data-images.　Furthermore,　the　data-images　are　used　to　train　the　SRGAN　model.　Finally,　the　trained　generator　can　be　used　to　reconstruct　the　low-frequency　sampling　data　into　the　high-frequency　sampling　data.　Numerical　experiments　have　been　carried　out　based　on　photovoltaic　(PV)　power　generation　time-series　data　from　the　State　Grid　Corporation　of　China　with　separately　reconstruction　of　the　irradiance　and　PV　power　datas.　The　results　demonstrate　the　superior　performance　of　the　proposed　method　compared　with　a　series　of　state-of-the-art　methods.　©　2021　IEEE.