Dynamic Temporal Graph Sequence Data for Resilience-Oriented Distribution Network Reconfiguration

This dataset comprises temporal dynamic graph sequences generated from power grid simulations focused on grid reconfiguration to enhance resilience. The simulations model failure propagation under varying conditions, with nodes assigned distinct failure probabilities. For each time step, the dataset captures the evolution of node states (functional or failed) and features critical to grid operations, such as pv_output, load_profile, load_dispatch, dg_output, loss, and voltage. Node types include sources, normal loads, and nodes with specific equipment like PVs, micro turbines, or shunt capacitors. The dataset is structured to support the training of dynamic graph neural networks, facilitating research on node feature prediction and edge dynamics under failure scenarios. Three distinct configurations are included, providing a robust foundation for modeling power grid resilience.

本数据集包含源自电网仿真的时序动态图序列（temporal dynamic graph sequences），该仿真聚焦于提升电网韧性的电网重构任务。仿真模型对不同工况下的故障传播（failure propagation）过程进行建模，并为各节点赋予差异化的故障发生概率。在每个时间步长中，本数据集记录了节点状态（正常运行或故障）以及电网运行关键特征的演化情况，这些特征包括光伏输出（pv_output）、负荷曲线（load_profile）、负荷调度（load_dispatch）、分布式电源输出（dg_output）、网损（loss）与电压（voltage）。节点类型涵盖电源节点、常规负荷节点，以及搭载特定设备的节点，例如光伏（PVs）、微型燃气轮机（micro turbines）或并联电容器（shunt capacitors）节点。本数据集的结构设计旨在支持动态图神经网络（dynamic graph neural networks）的训练工作，助力故障场景下的节点特征预测与边动态演化相关研究。本数据集包含三种不同的配置方案，可为电网韧性建模提供可靠的研究基础。