A power grid topology detection method based on edge graph attention neural network

Against the backdrop of growing power demand and market liberalization, operators optimize power transmission and system load balance through network reconfiguration by switching lines. However, the complex node connections and dynamic load variations exceed the capabilities of traditional algorithms, and data noise may cause detection errors, affecting grid dispatch and stability. To address this issue, a solution is proposed based on the Edge Graph Attention Neural Network (EGAT) model, providing an in-depth analysis of the reconfigured grid structure. The model employs a multi-head attention mechanism to integrate node and edge features from different layers, enhancing feature fusion and extracting critical topological information, thereby improving the accuracy and robustness of grid topology detection after reconfiguration. The method was tested on IEEE 14-bus, IEEE 39-bus, and IEEE 118-bus systems, achieving detection accuracies of 92.30 %, 90.14 %, and 87.25 %, respectively, significantly outperforming other neural network methods. However, the complexity of the model may result in additional computational overhead.