Towards Hierarchical GNNs for multi-grid power flow: generalization across operating scenarios
Paper recorded by Signals 4 on 2026-09-22 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23
Category: cs.AI · 人工智能 · first seen 2026-09-23
Abstract
Hierarchical latent communication improves the generalization of a multi-grid power-flow model to new operating scenarios. The module exchanges information through two reduced graphs within a GENCO-based corrective network. We compare Kron-derived transports, a same-anchor Quotient construction and a flat backbone in preliminary trainings of 200 epochs on three grid topologies, with three initiali
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Cite this page: Towards Hierarchical GNNs for multi-grid power flow: generalization across operating scenarios: the #20 most recent of 360 cs.AI papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/towards-hierarchical-gnns-for-multi-grid-power-flow-generalization-across-operat.html
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