Graph Neural Network-Based Node Clustering for Dual-Focused Power Network Partitioning

Partitioning the power system into smaller, manageable units facilitates better grid monitoring and control, thereby improving the grid’s stability and reliability. However, large-scale power networks consist of thousands of nodes and edges, which complicates the process of learning appropriate node embeddings and aggregating information from neighboring nodes. By representing power grids as undirected weighted graphs, this study proposes a novel power network partitioning approach using Graph Neural Networks (GNN). The proposed model simplifies the clustering objective by focusing on a single balancing term, which reduces computational complexity while maintaining competitive clustering performance. The power network is represented as a graph where the proposed GNN uses the normalized graph Laplacian, which effectively captures the complex connectivity of the nodes, instead of the traditional adjacency matrix. Active power levels serve as nodal attributes, ensuring that clusters represent both the physical and operational characteristics of the network. This dual-focused approach promotes a partitioning that is topologically coherent and functionally homogeneous, vital for enhanced grid management. When applied to the IEEE 14, 39, and 118 bus systems, the proposed method has successfully delineated coherent clusters of buses, underlining its potential for improving power grid management. The simulation results confirm the method’s efficacy and applicability.