发表机构
Department of Electrical Engineering, Indian Institute of Technology Roorkee（电子工程系，印度理工学院Roorkee）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究交流潮流问题中拓扑变化导致机器学习代理精度下降的问题，提出无梯度的上下文白化方法，通过重新估计白化适应新拓扑，在多个电力系统测试中大幅降低误差，提升适应速度，成本可并行化。
AI 中文摘要
机器学习的交流潮流（ACPF）问题代理可分摊固定网络上重复求解的成本，但线路停电改变拓扑时精度会下降一到两个数量级，这是一种算子偏移。现有方法通过目标拓扑数据和每个拓扑的梯度步骤来校正。本文提出上下文白化（ICW）方法，在由基础拓扑的前两个矩白化的输出空间中训练ACPF代理，并通过在新拓扑上几百个求解案例重新估计白化来将其适应到未见的N-1或N-2拓扑。这种适应是无梯度、无权重且与架构无关的。在IEEE 30、118和300总线系统的N-1和N-2突发事件下，ICW比固定代理将整体误差降低了6倍到28倍，最大程度减少了母线功率平衡失配。在部署规模上，ICW在精度上匹配或超过基于梯度的适应，同时适应速度快21倍到34倍，成本可在商品CPU核心上并行化。
英文摘要
Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of magnitude of accuracy when a line outage changes the topology. This degradation is an operator shift. The altered admittance matrix changes the input-to-output map, so identical inputs yield a different output distribution. Existing methods correct this with target-topology data and per-topology gradient steps. We ask whether the correction can instead be made statistical and gradient-free. We propose In-Context Whitening (ICW), which trains an ACPF surrogate in an output space whitened by the base topology's first two moments, and adapts it to an unseen N-1 or N-2 topology by re-estimating that whitening from a few hundred solved cases on the new topology. This adaptation is gradient-free, weight-free, and architecture-agnostic. We prove that among affine whiteners the unique choice that preserves the coordinate-wise semantics of the physical output vector is ZCA whitening, so within efficient invertible corrections, two moments are sufficient. Across the IEEE 30-, 118-, and 300-bus systems under N-1 and N-2 contingencies, ICW reduces overall error by 6$\times$ to 28$\times$ over frozen surrogates (up to 54$\times$ per-quantity under N-2) and cuts worst-bus power-balance mismatch by up to 30$\times$, with consistent gains across three backbones. At deployment scale it matches or beats gradient-based adaptation in accuracy while adapting 21$\times$ to 34$\times$ faster, with a cost that parallelizes on commodity CPU cores rather than requiring one GPU per contingency.
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