It　is　still　a　focus　of　research　to　detect　the　faulty　feeder　timely　and　accurately　in　resonant　grounding　distribution　systems.　The　conventional　methods　commonly　use　single　faulty　feeder　detection　methods,　such　as　wavelet　transform　method,　transient　energy　method,　and　the　fifth　harmonic　current　method,　etc.　However,　their　reliability　is　not　satisfied　due　to　the　partial　fault　information　is　considered.　A　novel　approach　to　identify　the　faulty　feeder　based　on　discrete　wavelet　packet　transform　(DWPT)　and　machine　learning　is　proposed　in　this　paper.　The　time-frequency　matrices　are　obtained　by　utilizing　the　DWPT　to　the　collected　transient　zero-sequence　current　signals　of　the　faulty　feeder　and　sound　feeders.　The　feature　vectors　will　be　extracted　manually　by　calculating　time-frequency　matrices　with　statistical　quantities.　The　two　classifiers　(Adaboost+CART　and　SVM)　are　trained　by　a　large　number　of　feature　vectors　under　various　kinds　of　fault　conditions　and　factors,　respectively.　The　faulty　feeder　detection　can　be　achieved　by　the　trained　two　classifiers.　A　PSCAD/EMTDC　simulator　is　established　to　simulate　a　practical　10-kV　resonant　grounding　distribution　system.　Simulation　results　of　the　testing　cases　validate　that　the　proposed　approach　of　fault　detection　is　able　to　achieve　good　identification　accuracy.　©　2017　IEEE.