Data underlying the publication: PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation

DOI:10.4121/b27152e4-4237-40f9-a72c-e6a1ca916960.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/b27152e4-4237-40f9-a72c-e6a1ca916960

Datacite citation style

Lin, Nan; Orfanoudakis, Stavros (2024): Data underlying the publication: PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/b27152e4-4237-40f9-a72c-e6a1ca916960.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Delft University of Technology logo

Usage statistics

112
views
507
downloads

Licence

CC BY 4.0

Interoperability

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.


If you use parts of this dataset, please cite as:


@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}

}

History

  • 2024-02-05 first online, published, posted

Publisher

4TU.ResearchData

Format

numpy arrays

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics, and Computer Science, Department of Electrical Sustainable Energy, Intelligent Electrical Power Grids Section

DATA

Files (15)