Accurate　and　reliable　fault　location　technology　is　essential　to　the　stable　operation　of　the　modular　multilevel　converter-high　voltage　direct　current　(MMC-HVDC)　system.　Aiming　at　the　difficulty　of　locating　high-resistance　ground　faults　on　MMC-HVDC　transmission　lines,　this　paper　proposes　a　method　for　fault　location　of　transmission　lines　based　on　wavelet　transform　and　deep　belief　network　(DBN).　First,　the　wavelet　transform　is　used　to　decompose　the　original　single　pole　ground　fault　voltage　waveform,　and　then　the　high-frequency　and　low-frequency　components　obtained　are　used　as　training　samples　to　train　different　DBN　models,　the　final　fault　location　results　are　obtained　by　superimposing　the　outputs　of　each　model　at　last.　The　+/-　250　kV　double-ended　MMC-HVDC　system　model　is　established　by　using　PSCAD/EMTDC,　which　can　simulate　the　faults　of　different　positions　and　different　transition　resistances.　In　order　to　verify　the　fault　location　performance　of　the　proposed　method,　it　is　compared　with　two　machine　learning　fault　location　methods.　The　results　show　that　this　method　can　accurately　and　reliably　locate　the　single　pole-to-ground　fault　of　the　transmission　line　with　transition　resistance　of　up　to　10　000　omega　at　low　sampling　frequency　of　20　kHz.　(c)　2020　Institute　of　Electrical　Engineers　of　Japan.　Published　by　Wiley　Periodicals　LLC.