Efficient generation of power system topology diagrams based on Graph Neural Network

Power system topology diagrams illustrate the physical and spatial relationship of system nodes and are widely used as a basic tool for displaying system structure. Well-presented topology diagrams provide better situational awareness for the operators, but their efficient generation remains a challenge. Existing approaches struggle to find a balance between visual aesthetics and the generation speed of the diagram. With the rapid changes in power system topology, there is a higher demand for the rendering speed of the graph data. To satisfy both the real-time requirement and the aesthetic quality, this paper proposes an integrated framework for efficiently generating power system topology diagrams. It consists of a Graph Neural Network (GNN) model and a graph fine-tuning model. This framework can directly optimize the raw topology diagram while preserving the relative positions of nodes in the initial layout. It achieves a decent trade-off between layout quality and computational expenses, enabling the generation of aesthetically satisfactory diagrams in a short time. Due to the strong generalization ability of GNN, the proposed model can be trained on small system datasets and used for inference on large systems. Case studies verify that the proposed GNN model can optimize the aesthetic metrics of topology diagram layouts within seconds to an average value of 0.55. Finally, it can be used in power system applications as a fundamental tool for topology diagram generation and optimization.