In　the　non-stationary　environments　of　distribution　networks,　where　operating　conditions　continually　evolve,　maintaining　reliable　high　impedance　faults　(HIF)　detection　is　a　significant　challenge　due　to　the　frequent　changes　in　data　distribution　caused　by　environmental　variations.　In　this　paper,　we　propose　a　novel　HIF　detection　method　based　on　incremental　learning　to　handle　non-stationary　data　stream　with　changing　distributions.　The　proposed　method　utilizes　stationary　wavelet　transform　(SWT)　to　extract　fault　characteristics　in　different　frequency　domains　from　zero-sequence　current　data.　Subsequently,　a　complex　mapping　from　signal　features　to　operational　conditions　is　established　using　backpropagation　neural　network　(BPNN)　to　achieve　online　detection　of　HIF.　Additionally,　signal　features　are　analyzed　using　density-based　spatial　clustering　of　applications　with　noise　(DBSCAN)　to　monitor　the　distribution　of　data.　After　encountering　multiple　distribution　changes,　an　incremental　learning　process　based　on　data　replay　is　initiated　to　evolve　the　BPNN　model　for　adapting　to　the　changing　data　distribution.　It　is　worth　noting　that　the　data　replay　mechanism　ensures　that　the　model　retains　previously　acquired　knowledge　while　learning　from　newly　encountered　data　distributions.　The　proposed　method　was　implemented　in　a　prototype　of　a　designed　edge　intelligent　terminal　and　validated　using　a　10　kV　testing　system　data.　The　experimental　results　indicate　that　the　proposed　method　is　capable　of　identifying　and　learning　new　distribution　data　information　within　non-stationary　data　stream.　This　enables　the　classifier　model　to　maintain　a　high　level　of　detection　accuracy　for　the　current　cycle　data,　effectively　enhancing　the　reliability　of　HIF　detection.　©　2023