In　industrial　applications,　the　existing　fault　location　methods　of　resonant　grounding　distribution　systems　suffer　from　low　accuracy　due　to　excessive　dependence　on　communication,　lack　of　field　data,　difficulty　in　artificial　feature　extraction　and　threshold　setting,　etc.　To　address　these　problems,　this　study　proposes　a　decentralized　fault　section　location　method,　which　is　implemented　by　the　primary　and　secondary　fusion　intelligent　switch　(PSFIS)　with　two　preloaded　algorithms:　autoencoder　(AE)　and　backpropagation　neural　network.　The　relation　between　the　transient　zero-sequence　current　and　the　derivative　of　the　transient　zero-sequence　voltage　in　each　section　is　analyzed,　and　its　features　are　extracted　adaptively　by　using　AE,　without　acquiring　network　parameters　or　setting　thresholds.　The　current　and　voltage　data　are　processed　locally　at　PSFISs　throughout　the　whole　procedure,　making　it　is　insusceptible　to　communication　failure　or　delay.　The　feasibility　and　effectiveness　of　the　approach　are　investigated　in　PSCAD/EMTDC　and　real-time　digital　simulation　system,　which　is　then　validated　by　field　data.　Compared　with　other　methods,　the　experiment　results　indicate　that　the　proposed　method　performs　well　in　various　scenarios　with　strong　robustness　to　harsh　on-site　environment　and　roughness　of　data.