Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator

Kim C, Conrad T, Karim R, Oelhaf J, Riebesel D, Maier A, Jäger J, Bayer S (2026)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2026

Publisher: IEEE

Series: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Proceedings

City/Town: Barcelona, Spain

Conference Proceedings Title: 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

URI: https://arxiv.org/abs/2509.22458

Abstract

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.

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How to cite

APA:

Kim, C., Conrad, T., Karim, R., Oelhaf, J., Riebesel, D., Maier, A.,... Bayer, S. (2026). Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator. In 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Barcelona, Spain: IEEE.

MLA:

Kim, Changhun, et al. "Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator." Proceedings of the 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Barcelona, Spain: IEEE, 2026.

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