Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures. Traditional power-flow-based tools for assessing cascading blackout risk are too slow to properly explore the space of possible failures and load/generation patterns. We add to the growing literature of faster graph-neural-network (GNN)-based techniques, developing two novel techniques for the estimation of blackout magnitude from initial grid conditions. First we propose several methods for employing an initial classification step to filter out safe "non blackout" scenarios prior to magnitude estimation. Second, using insights from the statistical properties of cascading blackouts, we propose a method for facilitating non-local message passing in our GNN models. We validate these two approaches on a large simulated dataset, and show the potential of both to increase blackout size estimation performance.

翻译：电网条件因可再生能源渗透率提高和极端天气事件频发而表现出更高的变异性，这增加了对可能导致灾难性级联故障场景进行筛选的难度。传统的基于潮流分析的级联停电风险评估工具因速度过慢，无法充分探索可能的故障及负荷/发电模式空间。我们为快速发展的图神经网络技术文献提供了新的贡献，提出了两种基于初始电网条件估计停电规模的新方法。首先，我们设计了多种方法，在规模估计之前引入初始分类步骤，以过滤掉安全的"非停电"场景。其次，基于级联停电的统计特性，我们提出了一种促进GNN模型中非局部消息传递的方法。我们在大规模模拟数据集上验证了这两种方法，并展示了它们提升停电规模估计性能的潜力。