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2019

Deep Generative Graph Distribution Learning for Synthetic Power Grids

Khodayar, Mahdi, Wang, Jianhui, Wang, Zhaoyu

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Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns.

Thus, power grid synthesis (PGS), i.e., creating realistic power grids that imitate actual power networks, has gained significant attention.
In this letter, we cast PGS into a graph distribution learning (GDL) problem where the probability distribution functions (PDFs) of the nodes (buses) and edges (lines) are captured.
A novel deep GDL (DeepGDL) model is proposed to learn the topological patterns of buses/lines with their physical features (e.g., power injection and line impedance).

Deep Generative Graph Distribution Learning for Synthetic Power Grids · Around

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X. Lu, K. Kuzmin, M. Chen, and B. K. Szymanski, “Adaptive modularity maximization via edge weighting scheme,” Information Sciences , vol. 424, pp. 55–68, 2018
Original
1901
Earlier work this paper cites.
K. M. Gegner, A. B. Birchfield, T. Xu, K. S. Shetye, and T. J. Overbye, “A methodology for the creation of geographically realistic synthetic power flow models,” in Power and Energy Conference at Illinois (PECI), 2016 IEEE , 2016, pp. 1–6
2016
Earlier work this paper cites.
A. B. Birchfield, T. Xu, K. M. Gegner, K. S. Shetye, and T. J. Overbye, “Grid structural characteristics as validation criteria for synthetic networks,” IEEE Transactions on power systems , vol. 32, no. 4, pp. 3258–3265, 2017
2017
Earlier work this paper cites.

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S. Soltan, A. Loh, and G. Zussman, “A learning-based method for generating synthetic power grids,” IEEE Systems Journal , no. 99, pp. 1–10, 2018
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S. Soltan, A. Loh, and g. Zussman, “Columbia university synthetic power grid with geographical coordinates,” Tech. Rep., 1 2018
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Cited alongside, same era.

Then

“IEEE benchmark systems,” www.labs.ece.uw.edu/pstca/, accessed: 2019-01-20
2019
Closest in time.
“Polish grid,” www.pserc.cornell.edu/matpower/, accessed: 2019-01-21
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