Feature　extraction　for　fault　signals　is　critical　and　difficult　in　all　kinds　of　fault　detection　schemes.　A　novel　simple　and　effective　method　of　faulty　feeder　detection　in　resonant　grounding　distribution　systems　based　on　the　continuous　wavelet　transform　(CWT)　and　convolutional　neural　network　(CNN)　is　presented　in　this　paper.　The　time-frequency　gray　scale　images　are　acquired　by　applying　the　CWT　to　the　collected　transient　zero-sequence　current　signals　of　the　faulty　feeder　and　sound　feeders.　The　features　of　the　gray　scale　image　will　be　extracted　adaptively　by　the　CNN,　which　is　trained　by　a　large　number　of　gray　scale　images　under　various　kinds　of　fault　conditions　and　factors.　The　features　extraction　and　the　faulty　feeder　detection　can　be　implemented　by　the　trained　CNN　simultaneously.　As　a　comparison,　two　faulty　feeder　detection　methods　based　on　artificial　feature　extraction　and　traditional　machine　learning　are　introduced.　A　practical　resonant　grounding　distribution　system　is　simulated　in　power　systems　computer　aided　design/electromagnetic　transients　including　DC,　the　effectiveness　and　performance　of　the　proposed　faulty　feeder　detection　method　is　compared　and　verified　under　different　fault　circumstances.