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, IEEE118 and Texas bus systemsThe Cascading failure dataset is in 'dataset_cascades' for the IEEE24, IEEE39, UK, and IEEE118 bus systems. The raw Cascading failures dataset for the Texas dataset is in 'raw.7z'.<br>

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）以及级联故障事件建模的综合性图数据集。针对潮流与最优潮流数据集的生成，本研究采用了MATPOWER工具；而对于级联故障数据集，则使用了名为Cascades的基于物理机理的级联故障模型。该模型可模拟电力电网内故障的传播过程，最终导致未满足用电需求（Demand Not Satisfied, DNS）。每个电网状态均以图结构表征：其中母线（包含负荷与发电机）作为节点，支路（包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；而针对级联故障分析任务，则基于该物理模型的输出结果赋予图级标签。该数据集可支撑多种机器学习任务，包括节点回归、图级多分类、二分类以及回归任务。此外，本数据集还为级联故障分析任务提供了真值解释，使其可作为评估图神经网络（Graph Neural Network, GNN）可解释性模型在图级任务中表现的基准数据集。<br>潮流与最优潮流数据集存放于`dataset_pf_opf`目录下，涵盖IEEE24、IEEE39、英国电网、IEEE118以及德克萨斯州共5类母线系统。<br>级联故障数据集存放于`dataset_cascades`目录下，涵盖IEEE24、IEEE39、英国电网以及IEEE118共4类母线系统；德克萨斯州母线系统的原始级联故障数据集则存放于`raw.7z`压缩包中。

搜集汇总

背景与挑战

背景概述

PowerGraph是一个综合性的电力系统图数据集，包含电力潮流、最优潮流和级联故障模拟数据，支持节点回归、图分类等多种机器学习任务，并特别提供级联故障的真实解释用于GNN可解释性研究。数据集覆盖IEEE24、IEEE39等多个电网系统。