Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914340044537856 |
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| author | Kim, Changhun Conrad, Timon Karim, Redwanul Oelhaf, Julian Riebesel, David Arias-Vergara, Tomás Maier, Andreas Jäger, Johann Bayer, Siming |
| author_facet | Kim, Changhun Conrad, Timon Karim, Redwanul Oelhaf, Julian Riebesel, David Arias-Vergara, Tomás Maier, Andreas Jäger, Johann Bayer, Siming |
| contents | Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of scenarios must be evaluated. However, current PIGNNs still need accuracy improvements at parity speed; in particular, the soft constraint on the physics loss is inoperative at inference, which can deter operational adoption. We address this with PIGNN-Attn-LS, combining an edge-aware attention mechanism that explicitly encodes line physics via per-edge biases to form a fully differentiable knownoperator layer inside the computation graph, with a backtracking line-search-based globalized correction operator that restores an operative decrease criterion at inference. Training and testing use a realistic High-/Medium-Voltage scenario generator, with NR used only to construct reference states. On held-out HV cases consisting of 4-32-bus grids, PIGNN-Attn-LS achieves a test RMSE of 0.00033 p.u. in voltage and 0.08 deg in angle, outperforming the PIGNN-MLP baseline by 99.5% and 87.1%, respectively. With streaming micro-batches, it delivers 2-5x faster batched inference than NR on 4-1024-bus grids. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator Kim, Changhun Conrad, Timon Karim, Redwanul Oelhaf, Julian Riebesel, David Arias-Vergara, Tomás Maier, Andreas Jäger, Johann Bayer, Siming Machine Learning Artificial Intelligence Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of scenarios must be evaluated. However, current PIGNNs still need accuracy improvements at parity speed; in particular, the soft constraint on the physics loss is inoperative at inference, which can deter operational adoption. We address this with PIGNN-Attn-LS, combining an edge-aware attention mechanism that explicitly encodes line physics via per-edge biases to form a fully differentiable knownoperator layer inside the computation graph, with a backtracking line-search-based globalized correction operator that restores an operative decrease criterion at inference. Training and testing use a realistic High-/Medium-Voltage scenario generator, with NR used only to construct reference states. On held-out HV cases consisting of 4-32-bus grids, PIGNN-Attn-LS achieves a test RMSE of 0.00033 p.u. in voltage and 0.08 deg in angle, outperforming the PIGNN-MLP baseline by 99.5% and 87.1%, respectively. With streaming micro-batches, it delivers 2-5x faster batched inference than NR on 4-1024-bus grids. |
| title | Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.22458 |