Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis

Mei Yang, Gao Qiu, Yong Wu, Junyong Liu, Nina Dai +3 more
4/29/2023
eess.SYcs.LG

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Abstract

The increasing scale of alternating current and direct current (AC/DC) hybrid systems necessitates a faster power flow analysis tool than ever. This letter thus proposes a specific physics-guided graph neural network (PG-GNN). The tailored graph modelling of AC and DC grids is firstly advanced to enhance the topology adaptability of the PG-GNN. To eschew unreliable experience emulation from data, AC/DC physics are embedded in the PG-GNN using duality. Augmented Lagrangian method-based learning scheme is then presented to help the PG-GNN better learn nonconvex patterns in an unsupervised label-free manner. Multi-PG-GNN is finally conducted to master varied DC control modes. Case study shows that, relative to the other 7 data-driven rivals, only the proposed method matches the performance of the model-based benchmark, also beats it in computational efficiency beyond 10 times.

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Cite this paper

@article{yang2023physicsguided,
  title  = {Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis},
  author = {Mei Yang and Gao Qiu and Yong Wu and Junyong Liu and Nina Dai and Yue Shui and Kai Liu and Lijie Ding},
  year   = {2023},
  eprint = {2305.00216},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.00216v1}
}

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