PowerGraph

We have created a comprehensive graph dataset that models power flow (PF), optimal power flow (OPF), and cascading failure events in power systems. To generate the dataset for PF and OPF, we utilized MATPOWER, and for the cascading failure events, we employed a physics-based cascading failure model called Cascades. This model simulates the propagation of failures within a power grid, resulting in unmet demand (DNS). Each power grid state is represented as a graph, where buses (including loads and generators) form the nodes and branches (including transmission lines and transformers) form the edges.For PF and OPF, we treat the problem as a regression task to predict electrical quantities at the node level. In contrast, for cascading failure analysis, we assign graph-level labels based on the outcomes of the physics-based model. This dual approach allows our dataset to support a variety of tasks, including node regression, graph-level multi-class classification, binary classification, and regression. Additionally, we provide ground-truth explanations for the cascading failure analysis, enabling our dataset to serve as a benchmark for evaluating GNN explainability models in graph-level tasks. The PF and OPF dataset is in 'dataset_pf_opf' for the IEEE24, IEEE39, UK, and IEEE118 bus systemsThe Cascading failure dataset is in 'dataset_cascades' for the IEEE24, IEEE39, UK, and IEEE118 bus systems.

本团队构建了一套涵盖电力系统潮流（Power Flow, PF）、最优潮流（Optimal Power Flow, OPF）以及连锁故障事件建模的全维度图数据集。针对潮流与最优潮流数据集的生成，本研究采用了MATPOWER工具；而针对连锁故障事件数据集，则使用了名为Cascades的基于物理机制的连锁故障模型。该模型可模拟电力电网内故障的传播过程，最终导致未满足需求（Demand Not Satisfied, DNS）。每个电网状态均以图结构表征：其中母线（含负荷与发电机）作为节点，支路（含输电线路与变压器）作为边。针对潮流与最优潮流任务，本研究将其视为节点级电气量预测的回归任务；而针对连锁故障分析任务，则基于上述物理模型的输出结果赋予图级标签。这种双任务设计使得本数据集可支撑多种下游任务，涵盖节点回归、图级多分类、二分类以及回归任务。此外，本数据集还为连锁故障分析任务提供了真实解释（ground-truth explanations），使其可作为图级任务中评估图神经网络（Graph Neural Network, GNN）可解释性模型的基准数据集。潮流与最优潮流数据集包含IEEE24、IEEE39、英国电网以及IEEE118母线系统，存储路径为`dataset_pf_opf`；连锁故障数据集同样包含上述四类母线系统，存储路径为`dataset_cascades`。