Data underlying the publication: PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation
收藏4TU.ResearchData2024-02-05 更新2026-04-23 收录
下载链接：
https://data.4tu.nl/datasets/b27152e4-4237-40f9-a72c-e6a1ca916960/1
下载链接
链接失效反馈官方服务：
资源简介：
Synthetic power flow dataset consist of three cases: 14-bus, 118-bus and 6470-bus. The line parameters, generator/load injections, voltage setpoints are randomly sampled based on the standard scenario. The 14-bus case consists of 100000 scenarios, 118-bus 50000 scenarios, and 6470-bus 30000 scenarios.<br>If you use parts of this dataset, please cite as:<br>@misc{lin2023powerflownet, title={PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation}, author={Nan Lin and Stavros Orfanoudakis and Nathan Ordonez Cardenas and Juan S. Giraldo and Pedro P. Vergara}, year={2023}, eprint={2311.03415}, archivePrefix={arXiv}, primaryClass={cs.LG}}
本合成电力潮流（power flow）数据集包含三类算例：14母线（bus）、118母线（bus）与6470母线（bus）系统。线路参数、发电机与负荷注入功率及电压设定值均基于标准场景随机采样生成。其中14母线算例包含100000个场景，118母线算例包含50000个场景，6470母线算例包含30000个场景。
若使用本数据集的部分内容，请按如下格式引用：
@misc{lin2023powerflownet,
title={PowerFlowNet：利用消息传递图神经网络（Message Passing GNNs）实现改进的电力潮流近似方法},
author={Nan Lin、Stavros Orfanoudakis、Nathan Ordonez Cardenas、Juan S. Giraldo及Pedro P. Vergara},
year={2023},
eprint={2311.03415},
archivePrefix={arXiv},
primaryClass={cs.LG}}
创建时间：
2024-02-05
