Data underlying the publication: PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation
收藏DataCite Commons2024-02-05 更新2024-07-03 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}}
提供机构：
4TU.ResearchData
创建时间：
2024-02-05
