Graph Neural Networks for Fast Contingency Analysis of Power Systems (2310.04213v3)

Abstract: The successful integration of machine learning models into decision support tools for grid operation hinges on effectively capturing the topological changes in daily operations. Frequent grid reconfigurations and N-k security analyses have to be conducted to ensure a reliable and secure power grid, leading to a vast combinatorial space of possible topologies and operating states. This combinatorial complexity, which increases with grid size, poses a significant computational challenge for traditional solvers. In this paper, we combine Physics-Informed Neural Networks with graph-aware neural network architectures, i.e., a Guided-Dropout (GD) and an Edge-Varying Graph Neural Network (GNN) architecture to learn the set points for a grid that considers all probable single-line reconfigurations (all critical N-1 scenarios) and subsequently apply the trained models to N-k scenarios. We demonstrate how incorporating the underlying physical equations for the network equations within the training procedure of the GD and the GNN architectures performs with N-1, N-2, and N-3 case studies. Using the AC Power Flow as a guiding application, we test our methods on the 6-bus, 24-bus, 57-bus, and 118-bus systems. We find that GNN not only achieves the task of contingency screening with satisfactory accuracy but does this up to 400 times faster than the Newton-Raphson power flow solver. Moreover, our results provide a comparison of the GD and GNN models in terms of accuracy and computational speed and provide recommendations on their adoption for contingency analysis of power systems.