发表机构
Vicomtech Foundation; University of the Basque Country (UPV/EHU)（维康科技基金会; 巴斯克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出层次化潜在通信模块，通过Kron缩减图改进多电网潮流模型，在训练拓扑上将电压误差降低85%，实现跨运行场景泛化，但跨拓扑泛化仍待提升。
AI 中文摘要
层次化潜在通信提高了多电网潮流模型对新运行场景的泛化能力。该模块通过基于GENCO的校正网络中的两个缩减图交换信息。我们在三个电网拓扑上进行了200轮初步训练，每个模型使用三个初始化种子，比较了Kron导出的传输、同锚点商构造和扁平骨干网络。评估使用每个电网200个新生成且预选的场景。在训练拓扑上，Kron将宏族平衡电压误差从5.660±0.899降至0.851±0.110：相对于扁平GENCO降低了85.0%，相对于达到1.235±0.225的商方法降低了31.0%。两种层次化模型在所有三个种子的每个训练拓扑上都优于基于训练解拟合的每母线均值。这些结果证明了在所研究拓扑内跨运行场景的泛化能力，且一组学习参数在电网间共享。对两个额外拓扑的评估将这一成就与跨拓扑泛化区分开来：在那种校准转移设置中，当前模型尚未超越拟合参考。本预印本展示了层次化通信作为多电网潮流学习组件的架构和初步证据，并将对未见拓扑的泛化作为下一个发展目标。
英文摘要
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.
