In　addressing　the　quantization　noise　challenge　in　high　impedance　fault　(HIF)　localization　within　resonant　distribution　networks,　we　propose　a　cutting-edge,　explainable　deep　learning　approach　that　significantly　advances　existing　methods.　This　approach　utilizes　differential　zero-sequence　voltage　(DZSV)　and　zero-sequence　current　(ZSC)　and　introduces　a　novel　＂Vague＂　classification　to　improve　localization　accuracy　by　effectively　managing　quantization　noise-distorted　signals.　This　approach　extends　beyond　the　conventional　binary　classification　of　＂Fault＂　and　＂Sound,＂　incorporating　a　multi-scale　feature　attention　(MFA)　mechanism　for　enriched　internal　explainability　and　applying　gradient-weighted　class　activation　mapping　(Grad-CAM)　to　visualize　critical　input　areas　precisely.　Our　model,　validated　in　an　industrial　prototype,　exhibits　unparalleled　adaptability　across　various　environmental　conditions,　including　environmental　noise,　variable　sampling　rates,　and　triggering　deviations.　Comparative　analysis　reveals　that　our　approach　outperforms　existing　methods　in　managing　diverse　fault　scenarios.　This　paper　presents　an　explainable　deep　learning　model　for　localizing　high　impedance　faults　(HIF)　in　resonant　distribution　networks,　addressing　challenges　posed　by　quantization　noise.　By　incorporating　a　novel　＇Vague＇　classification　and　a　multi-scale　feature　attention　mechanism,　the　model　improves　fault　localization　accuracy.　Validated　in　industrial　applications,　the　model　demonstrates　robustness　to　environmental　noise,　sampling　rate　variations,　and　triggering　deviations.image