The　transient　zero-sequence　current　of　each　feeder　in　a　resonant　grounding　system　is　characterized　by　nonlinearity　and　nonstationarity　when　a　single-phase-to-ground　fault　occurs.　Because　there　is　a　significant　difference　between　the　fault　transient　zero-sequence　current　waveforms　of　the　fault　feeder　and　the　sound　feeders,　a　new　fault　feeder　detection　method　is　presented,　based　on　a　time-frequency　matrix　(TFM)　and　polarity　distribution　matrix　(PDM)　singular　values　clustering　algorithm.　By　applying　a　Hilbert-Huang　transform　band-pass　filter　and　waveform　transformation　to　the　transient　zero-sequence　current　waveform　of　each　feeder,　the　TFM　and　PDM　can　be　constructed,　which　are　decomposed　by　singular-value　decomposition　(SVD).　Moreover,　the　normalized　singular　values　of　the　TFM　and　PDM　are　merged　together　and　are　used　to　form　the　amplitude-polarity　feature　matrix　(APFM).　Thus,　the　feature　quantities　including　the　amplitude　and　polarity　information　of　each　fault　transient　zero-sequence　current　waveform　are　obtained.　Then,　fuzzy　c-means　clustering　is　applied　to　the　APFM　so　as　to　detect　the　fault　feeder　by　dividing　the　fault　feeder　and　sound　feeders　into　two　categories　without　a　certain　threshold　setting.　Simulations　were　carried　out　via　PACAD/EMTDC　(R)　and　a　physical　system　under　various　kinds　of　fault　conditions　and　factors　including　asynchronous　sample,　two-point-grounding　fault,　and　arc　fault.　Simulated　results　show　that　the　proposed　method　has　the　characteristics　of　high　accuracy　and　reliability　in　earth　fault　feeder　detection.　(C)　2017　Elsevier　Ltd.　All　rights　reserved.