arXiv cs.AI· Carmine Delle Femine, Leire Garin Atxaga, Asier Diaz-Iglesias, Juan Pablo Maroto Herrera, Ane Miren Florez-Tapia, Marco Quartulli, Izaro Goienetxea Urkizu·· 15 小时前AI 评分24
分层 GNN 用于潮流计算：让物理结构塑造层级
Hierarchical GNNs for power flow: letting physics shape the hierarchy
AI 导读
分层潜在通信模块通过 GENCO 校正网络内的两个约简图交换信息，替换其中两步局部校正，使跨三个电网共享的潮流模型泛化到新运行场景。在训练拓扑上，Kron 的宏族平衡电压误差为 0.851±0.110，比按母线均值拟合的参考（1.747）低 51.3%，在 600 个新场景中 98.5% 低于该参考，并比 Quotient（1.235±0.225）低 31.0%。
正文 · 原文
Abstract:Hierarchical latent communication improves the generalization of a power-flow model, shared across three grids, to new operating scenarios. The module exchanges information through two reduced graphs inside the corrective network of GENCO, replacing two of its local correction steps. We compare Kron-derived transports, a same-anchor Quotient construction and the flat GENCO Base architecture, all trained under one protocol of our own with about a hundred times fewer optimizer updates per grid than GENCO's reference training: 200 epochs on three grid topologies, fewer than 1,900 training scenarios per grid and three initialization seeds per model. Evaluation uses 200 newly generated, preselected scenarios per grid. On the training topologies, Kron reaches a macro family-balanced voltage error of $0.851\pm0.110$, 51.3% below a per-bus mean fitted on training solutions (1.747). Kron is below this reference on 98.5% of the 600 fresh scenarios, and both hierarchical models outperform it on every training topology in all three seeds. The flat baseline reaches $5.660\pm0.899$ and does not outperform the reference on any training topology, so Kron's 85.0% reduction relative to it compares architectures within our training regime. Kron is also 31.0% below Quotient ($1.235\pm0.225$). These results demonstrate generalization across operating scenarios within the studied topologies, with one set of learned parameters shared across grids. On two topologies unseen in training, the current models do not yet outperform the fitted reference in calibrated transfer; extrapolation to new topologies is the next development objective.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.26603 [cs.LG] |
| (or arXiv:2609.26603v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.26603 arXiv-issued DOI via DataCite |
Submission history
From: Carmine Delle Femine [view email]
[v1]
Tue, 22 Sep 2026 15:41:11 UTC (168 KB)
[v2]
Fri, 25 Sep 2026 12:06:06 UTC (171 KB)
[v3]
Mon, 26 Oct 2026 16:52:07 UTC (126 KB)
来源：arXiv cs.AI · arxiv.org