For　a　variety　of　applications　within　power　systems,　the　precision　of　data　acquisition　is　of　paramount　importance.　However,　the　actual　data　may　be　corrupted　by　noise　in　the　process　of　measurement　or　transmission,　and　the　accuracy　of　dynamic　security　assessment　(DSA)　will　be　affected.　In　light　of　the　poor　interpretability　exhibited　by　traditional　machine　learning　(ML)　methods　in　denoising,　a　physics-informed　denoising　model　(PIDM)　for　dynamic　data　recovery　is　proposed.　The　differential　equations　of　physical　models　in　power　systems　are　employed　to　guide　the　training　of　PIDM.　They　are　transformed　into　physical　constraints　and　subsequently　incorporated　into　the　loss　function　of　stacked　denoising　autoencoder　(SDAE)　to　cleanse　noisy　data.　By　integrating　the　powerful　learning　capabilities　of　ML　with　the　rigorous　constraints　of　physical　laws,　the　noisy　data　recovered　by　PIDM　can　better　satisfy　the　dynamic　equations.　Consequently,　a　more　pronounced　denoising　effect　can　be　achieved.　The　improvement　of　the　PIDM　over　common　ML-based　models　is　explored　when　dealing　with　the　noisy　data　with　varying　degrees　of　interference　or　those　of　unexpected　faults.　The　effectiveness　is　validated　through　simulation　results　in　IEEE　39-bus　system　and　the　East　China　power　grid.　The　results　show　that　this　method　can　reduce　the　total　mean　square　error　(MSE)　of　the　recovery　of　noisy　data　to　at　least　65.27%　of　that　of　the　traditional　methods　under　the　same　conditions.　In　addition　to　demonstrating　superior　denoising　performance,　the　generalization　capability　under　diverse　noise　conditions　is　also　deemed　excellent.