Paper · arXiv
Power Flow Feasibility Assessment Using Variational Graph Autoencoders
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.
| Authors | Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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