Power Flow Feasibility Assessment Using Variational Graph Autoencoders

By Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo, Pere Barlet-Ros, Oriol Gomis-BellmuntSource:

[arXiv cs.LG](https://arxiv.org/list/cs.LG/recent)arXiv:2607.09122v1 Announce Type: new

Abstract: Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little

attentionhas been paid to the solution feasibility, which can be obtained by traditional solvers. This paper presents a Variational GraphAutoencoder(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.Get AI news in your inbox

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