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Rebuild AC Power Flow Models with Graph Attention Networks

arXiv 2509.22733 ↗First posted 25 Sep 2025eess.SY cs.AI cs.SYcited by 0

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Abstract (arXiv)

A full power flow (PF) model is a complete representation of the physical power network. Traditional model-based methods rely on the full PF model to implement power flow analysis. In practice, however, some PF model parameters can be inaccurate or even unavailable due to the uncertainties or dynamics in the power systems. Moreover, because the power network keeps evolving with possibly changing topology, the generalizability of a PF model to different network sizes and typologies should be considered. In this paper, we propose a PF rebuild model based on graph attention networks (GAT) by constructing a new graph based on the real and imaginary parts of voltage at each bus. By comparing with two state-of-the-art PF rebuild models for different standard IEEE power system cases and their modified topology variants, we demonstrate the feasibility of our method. Experimental results show that our proposed model achieves better accuracy for a changing network and can generalize to different networks with less accuracy discount.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 25 Sep 2025.

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References · 12

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2018Link Prediction Based on Graph Neural Networkscited by 832
  5. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  6. 2019Optimal Power Flow Using Graph Neural Networkscited by 63
  7. 2020Physics-Guided Deep Neural Networks for Power Flow Analysiscited by 33
  8. 2021Power Flow Balancing with Decentralized Graph Neural Networkscited by 20
  9. 2015Strong NP-hardness of AC power flows feasibilitycited by 14
  10. 2017Data-Driven Power Flow Linearization: A Regression Approachcited by 11
  11. 2017PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Gridscited by 4
  12. 2017Mapping Rule Estimation for Power Flow Analysis in Distribution Gridscited by 3

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