Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flow

תקציר מקורי באנגליתarXiv:2410.04818v2 Announce Type: replace-cross Abstract: We present PINCO, an unsupervised learning framework that integrates Graph Neural Networks with physics-informed neural networks for AC optimal power flow (AC-OPF) solutions. Unlike state-of-the-art unsupervised methods that require prescreened datasets containing only feasible instances, our approach operates on unfiltered data, including ill-conditioned cases. The framework addresses two critical gaps in the literature: (1) robustness to topology changes up to N-2 contingencies, and (2) detecting optimal power flow instances that are infeasible without relying on traditional solvers for data filtering. PINCO embeds physical laws into the learning process via augmented Lagrangian multipliers. In addition, it introduces a clustering