A　data-driven　predictive　controller　(DDPC)　is　proposed　for　the　solid-state　transformer　(SST)　to　manage　the　power　flows　in　a　distribution　network.　The　proposed　DDPC　is　derived　to　be　used　as　a　potential　substitute　to　vector　control　which　the　design　requires　knowledge　of　the　grid-connected　system.　The　strategy　uses　input　and　output　data　from　the　system,　the　ultra-local　model　based　on　a　neural　network　for　modeling　the　dynamic　of　the　current.　The　neural　network　enhances　the　robustness　of　the　controller　for　nonlinear　systems　and　external　disturbances.　To　improve　the　current　accuracy　of　the　controller,　a　discrete　space　vector　is　used　to　discretize　the　operating　region　of　the　converter　into　an　equivalent　virtual　multi-level　converter.　Simulation　results　demonstrate　the　effectiveness　of　the　proposed　control　in　managing　the　power　flows　in　a　medium-voltage　distribution　network.　©　2023　IEEE.