cs.LG · 2607.09122 Copy arXiv ID · Jul 10, 2026 Save Power Flow Feasibility Assessment Using Variational Graph Autoencoders Authors: Ferran Bohigas-Daranas , Hamid Latif-Martinez , Eduardo Prieto-Araujo , Pere Barlet-Ros , Oriol Gomis-Bellmunt
Abstract Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditional solvers. This paper presents a Variational Graph Autoencoder (VGAE) that detects the power flow solution feasibility, using the IEEE 118-bus case, to assess the validity of the solutions provided by AI-driven solvers.
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Robert Parker
Los Alamos National Laboratory, Los Alamos, NM, USA
This work formulates and solves optimization problems to generate input points that yield high errors between a neural network's predicted AC power flow solution and solutions to the AC power flow equations. We demonstrate this capability on an instance of the CANOS-PF graph neural network model, as implemented by the PF
Δ Δ Δ benchmark library, operating on a 14-bus test grid. Generated adversarial points yield errors as large as 3.7 per-unit in reactive power and 0.08 per-unit in voltage magnitude. When minimizing the perturbation from a training point necessary to satisfy adversarial constraints, we find that the constraints can be met with as little as an 0.04 per-unit perturbation in voltage magnitude on a single bus. This work motivates the development of rigorous verification and robust training methods for neural network surrogate models of AC power flow.