发表机构
State Key Laboratory of Power Grid Safety; China Electric Power Research Institute（电网安全国家重点实验室; 中国电力科学研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种自监督统一电网表示，分离物理描述与下游计算，冻结编码器可跨规模（至70,000母线）和四个任务复用，验证了其通用性与高效性。
AI 中文摘要
数据驱动的电力系统模型通常针对特定电网和计算任务开发，但当网络结构或分析目标发生变化时，其性能可能显著下降甚至失效。本文开发了一种统一的电网表示，将物理电网描述与下游计算分离。一个自监督编码器在共同潜在描述下，将每个电网表示为可变数量的固定维度节点、支路和全局向量。尽管仅在最多270个母线的系统上预训练，冻结的编码器无需适配即可迁移到70,000个母线的网络，其母线数量超过250倍，同时保持0.7919的母线对应准确率。该表示支持四个独立训练的下游任务：潮流计算、无功调整、运行工况生成和暂态稳定评估。在先前未见过的70,000母线电网上，潮流计算实现了相角平均误差0.108度、电压幅值平均误差3.8×10^-6 p.u.。无功调整在先前未见过的70,000母线电网上实现了75%的PSASP验证修正率，而运行工况生成在先前未见过的400至1,000母线电网族上，为82.2%的请求提供了至少一个PSASP验证的可行状态。对于暂态稳定评估，在匹配的训练条件下，冻结编码器的准确率与完整编码器适配的差距在0.8个百分点以内。这些结果表明，统一的电网表示可以学习一次，并在结构不同的电力系统和异构计算任务中保持不变地复用。
英文摘要
Data-driven power-system models are typically developed for specific grids and computational tasks, but their performance can deteriorate markedly or even fail when network structures or analytical objectives change. This paper develops a unified grid representation that separates physical-grid description from downstream computation. A self-supervised encoder represents each grid as a variable number of fixed-dimensional node, branch and global vectors under a common latent description. Although pretrained only on systems with at most 270 buses, the frozen encoder transfers without adaptation to a 70,000-bus network, more than 250 times larger in bus count, while preserving 0.7919 bus-correspondence accuracy. The same representation supports four independently trained downstream tasks: power-flow calculation, reactive-power adjustment, operating-condition generation and transient-stability assessment. On the previously unseen 70,000-bus grid, power-flow calculation achieves mean errors of 0.108$^{\circ}$ in phase angle and 3.8$\times$10\textsuperscript{-6} p.u. in voltage magnitude. Reactive-power adjustment achieved a 75\% PSASP-verified correction rate on the previously unseen 70,000-bus grid, while operating-condition generation delivered at least one PSASP-verified feasible state for 82.2\% of requests on previously unseen 400--1,000-bus grid families. For transient-stability assessment, the frozen encoder achieves accuracy within 0.8 percentage points of full encoder adaptation under matched training conditions. These results demonstrate that a unified power-grid representation can be learned once and reused unchanged across structurally different power systems and heterogeneous computational tasks.
