A GNN-Based Generative Model for Generating Synthetic Cyber-Physical Power System Topology

A GNN-Based Generative Model for Generating Synthetic Cyber-Physical Power System Topology

Liu, Y. (TU Delft Intelligent Electrical Power Grids)
Xie, H. (TU Delft Intelligent Electrical Power Grids)
Presekal, A. (TU Delft Intelligent Electrical Power Grids)
Stefanov, Alexandru (TU Delft Intelligent Electrical Power Grids)
Palensky, P. (TU Delft Electrical Sustainable Energy)

Electrical Sustainable Energy

2023

Synthetic networks aim at generating realistic projections of real-world networks while concealing the actual system information. This paper proposes a scalable and effective approach based on graph neural networks (GNN) to generate synthetic topologies of Cyber-Physical power Systems (CPS) with realistic network feature distribution. In order to comprehensively capture the characteristics of real CPS networks, we propose a generative model, namely Graph-CPS, based on graph variational autoencoder and graph recurrent neural networks. The method hides the sensitive topological information while maintaining the similar feature distribution of the real networks. We used multiple power and communication networks to prove and assess the effectiveness of the proposed method with experimental results.

Cyber-physical systems
graph neural networks
synthetic networks

http://resolver.tudelft.nl/uuid:971c7c1a-bc35-499e-bd2a-08d362760d55

https://doi.org/10.1109/TSG.2023.3304134

2024-02-14

1949-3061

IEEE Transactions on Smart Grid, 14 (6), 4968-4971

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Y. Liu, H. Xie, A. Presekal, Alexandru Stefanov, P. Palensky