Abstract：Due to the extensive addition of new energy systems，the number and types of power quality

disturbances in the system are also increased accordingly. However，the traditional power quality disturbance

（PQD）classification method has the problem of low accuracy and efficiency，and it is difficult to adapt to the

existing power quality management of power systems with high new energy penetration. Therefore，a PQD

classification method based on graph convolutional neural networks（GCNNs）and Gramian angular field（GAF）

was proposed. First，the original PQD signal was normalized and polar coordinate transformation was processed，

then GAF was used to graphically transform different kinds of PQD one-dimensional signals to generate twodimensional images containing different PQD features，and finally，GCNNs were used to train and classify the

different kinds of PQD images to achieve the classification of different PQDs. In the experiment part，the IEEE-39

node system was used to simulate and simulate different types of PQD curves，and the method proposed was used

for verification. The experiment results show that the proposed method can automatically extract and optimize the

features，and meet the high efficiency and accuracy of PQD identification and classification.

      Key words：power quality disturbance（PQD）；graph convolutional neural networks（GCNNs）；Gramian

angular field（GAF）；disturbance classification