Power Flow Balancing With Decentralized Graph Neural Networks
نویسندگان
چکیده
We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as supervised vertex regression task, where GNN trained predict current and injections at each grid branch that yield flow balance. By representing line graph with branches vertices, we can train accurate robust changes topology. In addition, by using specialized layers, are able build very deep architecture accounts for large neighborhoods graph, while implementing only localized operations. perform three different experiments evaluate: i) benefits of rather than global operations tendency models oversmooth quantities nodes; ii) resilience perturbations topology; iii) capability model simultaneously multiple topologies consequential improvement generalization new, unseen proposed efficient and, compared other solvers learning, not physical components, but also
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ژورنال
عنوان ژورنال: IEEE Transactions on Power Systems
سال: 2023
ISSN: ['0885-8950', '1558-0679']
DOI: https://doi.org/10.1109/tpwrs.2022.3195301