面向电力系统基础模型的可信层：应用于N-k contingency筛查
Trustworthiness Layer for Foundation Models in Power Systems: Application to N-k Contingency Screening
AI总结：
本文提出一种通用可信层，为电力系统基础模型提供统计有效的预测区间，通过分层置信预测和核加权置信预测方法提升N-k contingency筛查的可靠性，减少误报并提高关键违规检测率。
摘要：
我们提出了一种模型无关的可信层，为电力系统基础模型（FM）提供统计有效的预测区间。该层提供两种校准方法：(i) 分层置信预测（SCP），按故障严重性和电网元件划分残差；(ii) 核加权置信预测（KCP），通过场景表示将校准局部化到每个测试场景，从而得到更紧的近似条件界。以GridFM为例，我们在IEEE 24-和118节点系统上展示了该框架在N-k contingency筛查中的应用。可信层确保在N-k层级上超过90%的所有关键违规都被捕捉，同时在保持直流功率流的误报率减少至五分之一的情况下，最小化遗漏检测。与底层FM相比，该方法的计算开销极低，从而实现了超越常规N-1筛查的可靠大规模安全评估。
英文摘要：
We propose a model-agnostic trustworthiness layer that equips any foundation model (FM) for power systems with statistically valid prediction intervals. The layer offers two calibration approaches: (i) stratified conformal prediction (SCP), which partitions residuals by contingency severity and grid element, and (ii) kernel-weighted conformal prediction (KCP), which localizes the calibration to each test scenario via scenario representations, yielding tighter, approximately conditional bounds. Using GridFM as a guiding example, we demonstrate the framework on N-k contingency screening for IEEE 24- and 118-bus systems. The trustworthiness layer ensures that over 90% of all critical violations are captured across N-k levels, minimizing missed detections while maintaining up to 5 times fewer false alarms than DC Power Flow. With negligible computational overhead over the underlying FM, this approach enables reliable large-scale security assessment beyond routine N-1 screening.
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