Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy transition poses new challenges for the decision-makers and system operators. Therefore, we must develop grid analysis algorithms to ensure reliable operations. These key tools include power flow analysis and system security analysis, both needed for effective operational and strategic planning. The literature review shows a growing trend of machine learning (ML) models that perform these analyses effectively. In particular, Graph Neural Networks (GNNs) stand out in such applications because of the graph-based structure of power grids. However, there is a lack of publicly available graph datasets for training and benchmarking ML models in electrical power grid applications. First, we present PowerGraph, which comprises GNN-tailored datasets for i) power flows, ii) optimal power flows, and iii) cascading failure analyses of power grids. Second, we provide ground-truth explanations for the cascading failure analysis. Finally, we perform a complete benchmarking of GNN methods for node-level and graph-level tasks and explainability. Overall, PowerGraph is a multifaceted GNN dataset for diverse tasks that includes power flow and fault scenarios with real-world explanations, providing a valuable resource for developing improved GNN models for node-level, graph-level tasks and explainability methods in power system modeling. The dataset is available at https://figshare.com/articles/dataset/PowerGraph/22820534 and the code at https://github.com/PowerGraph-Datasets.

翻译：电网作为现代社会至关重要的关键基础设施，其设计需确保在多样化运行条件与故障场景下保持稳定运行。当前能源转型进程为决策者与系统运营商带来了全新挑战，因此必须开发电网分析算法以保障运行可靠性。其中潮流分析与系统安全分析作为核心工具，是实现高效运行与战略规划的基础。文献综述表明，采用机器学习模型高效执行此类分析的趋势日益显著。特别地，由于电网本身具有图结构特性，图神经网络在此类应用中展现出独特优势。然而，当前电力电网应用领域仍缺乏用于训练和评估机器学习模型的公开图数据集。本研究首先提出PowerGraph数据集，该数据集包含针对以下任务的图神经网络专用数据：i）潮流分析，ii）最优潮流分析，iii）电网级联故障分析。其次，我们为级联故障分析提供了真实解释数据。最后，我们对节点级与图级任务及可解释性方法进行了完整的图神经网络基准测试。总体而言，PowerGraph是一个面向多任务场景的综合性图神经网络数据集，涵盖包含真实解释的潮流与故障场景，为电力系统建模中节点级任务、图级任务及可解释性方法的图神经网络模型开发提供了宝贵资源。数据集可通过https://figshare.com/articles/dataset/PowerGraph/22820534获取，相关代码发布于https://github.com/PowerGraph-Datasets。