After　single-phase-to-ground　fault　(SPGF)　occurs　in　the　resonant　grounding　system,　it　will　produce　transient　electrical　quantities　with　abundant　characteristics,　such　as　transient　zero-sequence　current,　transient　three-phase　current,　and　transient　zero-sequence　voltage.　At　this　point,　there　is　a　great　discrepancy　between　transient　zero-sequence　current　and　transient　three-phase　current　sudden-change　at　upstream　and　downstream　detection　nodes,　while　those　at　detection　nodes　on　the　same　side　are　similar.　Besides,　the　trend　of　zero-sequence　current　at　upstream　detection　nodes　and　zero-sequence　voltage　is　opposite,　while　the　trend　of　them　downstream　is　similar.　On　this　basis,　this　paper　presents　a　new　method　for　locating　SPGF,　which　is　based　on　the　convolutional　deep　belief　network　(CDBN).　Firstly,　transient　zero-sequence　voltage,　transient　zero-sequence　current,　and　transient　three-phase　current　sudden-change　are　decomposed　and　reconstructed　by　discrete　wavelet　packet　transform　(DWPT)　to　obtain　a　time-frequency　matrix.　Then,　taking　it　as　input　of　CDBN　to　autonomously　extract　fault　characteristics,　and　fault　detection　nodes　are　divided　into　upstream　and　downstream　detection　nodes.　Finally,　combined　with　network　topology,　fault　location　can　be　realized.　The　simulation　test　and　data　analysis　show　this　method　can　identify　fault　segments　accurately　and　reliably,　and　has　good　adaptability　and　application　value.　©　2022　Elsevier　B.V.