Research on the Application of Graph Neural Network in Topology Optimization of Distribution Network

Graph Neural Networks (GNN) is a type of neural network specifically designed for processing graph structured data, which is very suitable for topology optimization in power systems. GNN can effectively capture the relationships between nodes and edges in network topology through graph convolution operations, providing accurate representation and analysis capabilities of network structure. This article provides a detailed introduction to the construction process of distribution network topology models and graph neural network models, including the construction of datasets, model training and optimization methods, and the clarification of feature extraction methods. The effectiveness of GNN model in distribution network topology optimization was verified through case studies. By conducting in-depth discussions on the experimental results, analyzing the differences between the results and expectations, proposing directions for model improvement, and exploring future research directions and possibilities.