Internal　overvoltage　accidents　in　the　distribution　network　are　likely　to　cause　an　equipment　insulation　breakdown　and　result　in　system　power　outages　and　economic　losses.　Therefore,　an　internal　overvoltage　identification　method　based　on　the　time-frequency　atomic　decomposition　is　investigated　in　this　study.　Firstly,　the　overvoltage　waveforms　are　divided　into　four　time　periods.　Then,　the　waveforms　during　these　four　time　periods　are　decomposed　by　the　atomic　decomposition　algorithm　to　obtain　the　effective　atoms　from　the　waveforms.　Moreover,　the　root-mean-square　(RMS)　value　of　the　zero-sequence　voltage,　the　dominant　atom　frequency,　the　total　relative　matching　degree,　and　the　effective　atom　frequency　are　extracted　as　major　features.　In　addition,　the　layered　identification　of　the　overvoltage　types　can　be　realized　by　combining　the　corresponding　identification　criteria.　The　salient　advantage　is　the　features　with　low　dimension　and　a　high　degree　of　discrimination.　Various　overvoltage　types　can　be　identified　just　by　the　corresponding　thresholds,　and　it　is　easier　to　deploy　in　the　field　than　conventional　methods　based　on　classifier　training.　The　effectiveness　of　the　proposed　method　is　verified　by　experimental　results,　and　it　concludes　that　the　proposed　algorithm　has　high　accuracy　and　strong　adaptability.