The　existing　single-phase　grounding　(SPG)　fault　section　location　methods　typically　suffer　from　difficulty　in　feature　selection,　limited　feeder　terminal　units　(FTUs)　configuration,　and　excessive　dependence　on　communication,　which　weaken　their　generalization　and　robustness.　To　overcome　these　challenges,　an　SPG　fault　section　location　approach　based　on　feature　subset　optimization　is　proposed.　First,　the　relation　between　the　position　of　FTU　and　its　three-phase　current　variation　is　analyzed,　and　its　fault　features　are　extracted　to　construct　the　candidate　feature　sets　as　feature　subset　optimization　objects.　Then,　genetic　algorithm　and　support　vector　machine　(SVM)　are　combined　to　select　the　optimal　feature　subset　with　small　dimensions　and　recognition　error,　which　avoids　the　empirical　errors　of　artificial　feature　selection.　To　reduce　the　cumulative　errors,　the　SVM　hyperparameters　are　simultaneously　optimized.　Finally,　the　SVM　model　is　trained　based　on　the　optimal　feature　subset　and　hyperparameters.　In　the　absence　of　zero-sequence　current　measurement,　three-phase　currents　measured　by　FTU　are　locally　processed　to　locate　the　fault　section　by　the　trained　SVM.　The　experimental　results　verified　the　effectiveness　and　feasibility　of　the　proposed　method.　In　this　paper,　a　single-phase　grounding　fault　section　location　method　is　proposed　by　using　genetic　algorithm　and　support　vector　machine　(SVM)　to　achieve　feature　subset　optimization.　In　the　process　of　section　identification,　the　empirical　error　caused　by　artificially　selecting　features　is　avoided,　and　cumulative　errors　of　multiple　links　such　as　feature　selection,　parameter　optimization,　and　model　training　are　reduced.　Additionally,　the　communication　and　measurement　requirements　are　reduced　since　feeder　terminal　unit　only　needs　to　measure　local　three-phase　current　information.　image