AI 中文总结
该研究提出W2V预测模型框架，用于筛选触发电网高压事件的关键天气场景，在6717节点合成得克萨斯系统实验验证了其有效性，发现最脆弱场景具风主导扰动特征。
AI 中文摘要
本文提出一种关键天气场景筛选框架，用于识别可触发电网高压（HV）事件的天气状况。不同于仅针对元件级停运风险的传统天气感知事故分析，该框架将天气场景作为电网级电压越限的潜在驱动因素进行筛选。给定一个非关键天气场景，我们通过可微天气-电压（W2V）预测模型在紧凑潜在空间上进行梯度更新，以寻找高维天气空间中的扰动，从而最大化预定义的电压临界性得分。具体而言，采用非负约束实现物理一致的扰动，同时采用基于L1范数的约束实现有界扰动；后者可促进稀疏且可解释的扰动，且该统一预算也能生成不同天气场景下的敏感性感知脆弱性排名。在6717节点合成得克萨斯系统上的数值实验有效证明了天气不确定性触发HV事件的潜力，且该潜力无法通过单个天气场景的电压分析来表征。值得注意的是，最脆弱场景的特征是以高风电容量区域集中的风主导扰动模式，这与实际潮流数据的观测结果及实际系统运行经验一致。
英文摘要
This paper proposes a critical weather scenario screening framework for identifying weather conditions that can trigger high-voltage (HV) events in the power grid. Unlike conventional weather-aware contingency analysis limited to component-level outage risk, our framework screens weather scenarios as potential drivers of grid-level voltage violations. Given a non-critical weather scenario, we seek the perturbation over the high-dimensional weather space to maximize a pre-defined voltage criticality score, by using a differentiable weather-to-voltage (W2V) predictive model to facilitate the gradient update over a compact latent space. Specifically, a non-negativity constraint is used for achieving physically-consistent perturbations, with another L1-norm based constraint for bounded perturbation. The latter could promote sparse and interpretable perturbations, and this uniform budget also yields a sensitivity-aware vulnerability ranking across different weather scenarios. Numerical experiments on a 6717-bus synthetic Texas system have effectively demonstrated the potential of weather uncertainty in triggering HV events, and this potential cannot be represented by the voltage analysis of individual weather scenarios. Interestingly, the most vulnerable scenarios are characterized by wind-dominated perturbation patterns concentrated in high wind-capacity regions, coinciding with observations from actual power flow data and experiences in real system operations.
Comments6 pages, 2 figures. Accepted to IEEE SmartGridComm 2026
