用于IEEE 9节点系统暂态稳定评估的基准图数据集：含20000种场景及完整发电机轨迹
A Benchmark Graph Dataset for Transient Stability Assessment of the IEEE 9-Bus System: 20,000 Scenarios with Full Generator Trajectories
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中文总结 AI 辅助
该研究发布了含20000种场景的IEEE 9节点系统暂态稳定评估基准图数据集，支持多种电力系统相关任务及不同学习方法的对比。
中文摘要 AI 辅助
暂态稳定评估用于判断电力系统在遭受大扰动后是否能保持同步运行。机器学习代理模型可加速该过程，但因缺乏结合动态真实值、网络图结构及电机参数的开放数据集，相关研究进展受限。本文发布了一个基准数据集，包含IEEE 9节点系统上的20000种三相接地故障场景，每种场景将交流潮流运行点与故障后响应的详细电磁暂态模拟相结合。每条记录提供：作为属性图的网络（9个节点、18条有向支路、10个节点特征和12条边特征）、3台发电机的完整转子角和转速轨迹、静态电机常数、故障描述，以及基于惯性中心的二元稳定标签。18个故障位置的负载和发电规模跨度大，使数据集类别接近平衡（48.96%为稳定场景，51.04%为不稳定场景）。发电过程是确定性的，通过固定随机种子和公开代码可完全复现。该数据集在IEEE DataPort上以永久DOI发布，支持稳定分类、轨迹预测、裕度和临界切除时间估计，以及拓扑感知、基于物理的和混合学习方法的对比研究。
英文摘要
Transient stability assessment determines whether a power system retains synchronism after a large disturbance. Machine-learning surrogates can accelerate it, but progress is limited by the lack of open datasets that combine dynamic ground truth, network graph structure, and machine parameters. We release a benchmark of 20,000 three-phase-to-ground fault scenarios on the IEEE 9-bus system. Each scenario couples an AC power-flow operating point with a detailed electromagnetic-transient simulation of the post-fault response. Every record provides the network as an attributed graph (nine buses, eighteen directed branches, ten node and twelve edge features), the full rotor-angle and speed trajectories of the three generators, the static machine constants, the fault description, and a center-of-inertia binary stability label. Wide load and generation scalings across eighteen fault locations yield a near-balanced distribution (48.96\% stable, 51.04\% unstable). Generation is deterministic and fully reproducible through fixed seeds and public code. The dataset is distributed on IEEE DataPort under a persistent DOI and supports stability classification, trajectory prediction, margin and critical-clearing-time estimation, and the comparison of topology-aware, physics-based, and hybrid learning methods.
