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

• URL: http://arxiv.org/abs/2509.22458v1
• Date: Fri, 26 Sep 2025 15:09:26 GMT
• Title: Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
• Abstract summary: Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers.<n>PIGNN-Attn-LS achieves a test RMSE of 0.00033 p.u. in voltage and 0.08$circ$ in angle, outperforming the PIGNN-MLP baseline by 99.5% and 87.1%, respectively.
• Score: 7.927606793404479
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace classic Newton--Raphson (NR) solvers, especially when thousands of scenarios must be evaluated. However, current PIGNNs still need accuracy improvements at parity speed; in particular, 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, capturing the grid's anisotropy, 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$^\circ$ in angle, outperforming the PIGNN-MLP baseline by 99.5\% and 87.1\%, respectively. With streaming micro-batches, it delivers 2--5$\times$ faster batched inference than NR on 4--1024-bus grids.

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