In　order　to　gradually　realize　the　new　power　system,　a　large　number　of　power　electronic　components　have　been　put　into　use　in　the　power　grid.　As　a　result,　the　power　quality　problem　becomes　more　and　more　serious,　which　is　mainly　manifested　in　the　compound　of　power　quality　disturbances　(PQDs)　and　the　decrease　in　the　applicability　of　traditional　identification　algorithms.　To　solve　this　problem,　the　visual　trajectory　circle　technology　was　proposed,　which　could　transform　the　one-dimensional　disturbance　signals　into　the　two-dimensional　trajectory　circle　images　with　obvious　shape　characteristics.　These　images　were　input　to　the　depth　residual　network　(ResNet)　for　autonomous　feature　learning　and　classification.　Firstly,　Hilbert　transform　(HT)　was　applied　to　the　complex　power　quality　disturbances　to　get　the　envelope　sequence　based　on　sampling　time.　Then,　taking　the　amplitude　as　the　polar　diameter　and　the　instantaneous　phase　corresponding　to　the　polar　angle,　the　trajectory　circle　images　were　obtained　in　polar　coordinates.　At　last,　they　were　input　to　the　optimal　model　of　the　ResNet18　to　learn　how　to　classify　PQDs.　In　order　to　verify　the　effectiveness　of　the　proposed　algorithm,　the　classifications　of　PQDs　were　conducted　through　in　the　simulation　and　experiment　in　this　paper.　The　results　show　that　the　proposed　method　cannot　only　better　extract　the　complex　PQDs　feature,　but　also　effectively　overcome　the　shortcomings　of　traditional　methods,　such　as　difficulties　in　poor　anti-noise　performance　and　etc.　The　purpose　of　high　precision　PQDs　classification　can　be　achieved.　©2022　Chin.Soc.for　Elec.Eng.