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
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
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.ResearchDataFormat
numpy arraysAssociated peer-reviewed publication
PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow ApproximationReferences
Organizations
TU Delft, Faculty of Electrical Engineering, Mathematics, and Computer Science, Department of Electrical Sustainable Energy, Intelligent Electrical Power Grids SectionDATA
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