As　the　penetration　of　renewable　energy　increases　rapidly,　the　power　quality　disturbance　(PQD)　is　becoming　more　and　more　complex,　making　it　difficult　for　traditional　methods　to　accurately　identify　the　PQD　and　locate　the　time　interval.　To　address　this　problem,　this　paper　proposes　a　PQD　point　classification　and　time　interval　identification　method　based　on　the　incorporation　of　multi-level　attention　mechanism.　The　classification　model　is　constructed　by　using　convolutional　neural　network　(CNN)　with　the　local　feature　attention　mechanism　(LFAM)　and　the　dual-scale　attention　mechanism　(DSAM).　LFAM　tracks　changes　in　amplitude　by　analyzing　the　envelope　and　selectively　amplifies　local　features　in　the　signal　waveform　using　weighted　techniques.　On　the　other　hand,　DSAM　facilitates　the　model　in　identifying　the　significance　of　features　from　both　the　channel　and　neuron　perspectives.　Finally,　each　sampling　point　is　classified　in　the　form　of　multiclass-multioutput,　based　on　which　the　time　interval　is　also　identified.　To　validate　the　effectiveness　of　the　proposed　method,　a　simulation　dataset　with　63　PQD　types　is　established.　The　average　classification　accuracy　of　the　proposed　model　is　99.10%　in　a　30dB　white　noise　environment,　and　the　time-detection　errors　are　all　in　the　millisecond　range,　which　has　better　generalization　performance　and　robustness　than　other　deep　learning　models.　Additionally,　a　hardware　platform　utilizing　an　AC　power　supply　is　developed　to　assess　the　performance　of　the　model.　The　model　achieves　an　average　accuracy　of　99.03%　on　this　platform,　further　verifying　the　reliability　of　the　proposed　method.　©2024　Chin.Soc.for　Elec.Eng.