To　enhance　the　generalization　and　explainability　of　data-driven　models　in　fault　detection,　this　study　introduces　a　cutting-edge　detection　approach　anchored　in　explainable　incremental　learning　for　high　impedance　faults　(HIFs).　Leveraging　the　discrete　wavelet　transform,　our　method　discerns　the　wavelet　coefficients　of　the　zero　sequence　current,　offering　a　quantitative　lens　to　view　HIFs　via　the　calculation　of　the　standard　deviation.　In　succession,　an　artificial　neural　network　(ANN)　is　refined　using　a　regularization-based　principle.　This　guiding　principle　charts　the　model＇s　evolutionary　path,　emphasizing　weight　regularization　and　incorporating　penalty　terms　into　the　loss　function.　Such　an　approach　dynamically　optimizes　model　parameters,　ensuring　the　assimilation　of　novel　knowledge　found　in　waveform　data　streams　while　safeguarding　against　the　detrimental　effects　of　forgetting　prior　knowledge.　The　robustness　of　the　proposed　methodology　is　corroborated　using　the　PSCAD/EMTDC　platform　and　real-world　field　data.　In　addition,　this　paper　delve　into　a　comprehensive　explainable　analysis　using　Shapley＇s　additive　attribution　theory,　with　an　objective　to　elucidate　model　explainability　from　both　a　holistic　and　granular　viewpoint.