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.<br>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.

本研究构建了一套涵盖电力系统潮流（Power Flow，PF）、最优潮流（Optimal Power Flow，OPF）与连锁故障事件建模的综合图数据集。针对PF与OPF数据集的生成，本研究采用了MATPOWER工具；而连锁故障事件数据集则依托名为Cascades的物理驱动连锁故障模型构建。该模型可模拟电力系统内故障的传播过程，最终导致未满足负荷需求（Demand Not Satisfied，DNS）。每个电力系统状态均以图结构表征：母线（含负荷与发电机组）作为图节点，支路（含输电线路与变压器）作为图边。针对PF与OPF任务，本研究将其视为节点级电气量预测的回归任务；与之相对，连锁故障分析任务则基于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值解释，使其可作为图级任务下评估图神经网络（Graph Neural Network，GNN）可解释性模型的基准数据集。
PF与OPF数据集存储于'dataset_pf_opf'路径下，涵盖IEEE24、IEEE39、UK以及IEEE118母线系统。
连锁故障数据集存储于'dataset_cascades'路径下，涵盖IEEE24、IEEE39、UK以及IEEE118母线系统。

搜集汇总

背景与挑战

背景概述

PowerGraph是一个综合性的图数据集，包含电力流/最优电力流模拟和级联故障事件数据，覆盖多种电力系统模型。该数据集支持多种机器学习任务，并特别提供了级联故障的真实解释数据，可用于GNN可解释性研究。