Power Flow Feasibility Assessment Using Variational Graph Autoencoders
cs.LG, cs.SY, eess.SY
Submitted: 2026-07-10
Updated: 2026-07-10
Comments: Conference
Journal ref: ISGT 2026
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: 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,
Terminology
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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