Given　that　the　manual　feature　selection　process　is　cumbersome　and　not　sufficiently　accurate,　a　classification　method　of　Power　Quality　Disturbance　(PQD)　based　on　a　Gramian　Angular　Field　(GAF)　and　a　Convolutional　Neural　Network　(CNN)　is　proposed　when　designing　the　power　quality　disturbance　classifier.　First,　one-dimensional　power　quality　disturbance　signals　are　mapped　to　two-dimensional　images.　Then　a　network　framework　suitable　for　power　quality　disturbance　classification　is　constructed　based　on　the　existing　neural　network.　Finally,　two-dimensional　images　are　taken　as　input,　and　the　CNN　will　automatically　extract　features　from　the　massive　disturbance　samples　and　classify　them.　Simulation　results　show　that　this　method　has　good　classification　performance　in　noisy　data,　and　it　is　an　effective　power　quality　disturbance　classification　method.　©　2021　Power　System　Protection　and　Control　Press.