Power System Transient Stability Assessment Based on Graph Convolutional Network and Hierarchical Graph Pooling with Structure Learning

At present, the research of power system transient stability assessment based on artificial intelligence mostly takes euclidean structure data as input. In order to consider the influence of system topology on power system transient stability, a model based on graph convolutional neural network and hierarchical graph pooling with structure learning for power system transient stability assessment is proposed. Firstly, the power system is deconstructed. Taking the buses as the nodes and the transmission lines as the edges, and then a graph which is a typical non-euclidean structure data is created. Secondly, combined with the idea of graph deep learning, the proposed GCN+HGP-SL model is used to extract the features of power flow data formed after the deconstruction and establish the mapping relationship between the features and the power system transient stability. HGP-SL includes two steps: dropping the sampling and learning the structure between nodes, which aims to catch the important nodes without destroying the structure itself. Finally, to evaluate the proposed model, the performance evaluation index system is established, and the control group is selected. The influence of various factors on the model is analyzed with the experiment. The experiment shows that the proposed model has better comprehensive performance.