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.

翻译：物理信息图神经网络（PIGNN）已成为一种快速的交流潮流求解器，能够替代经典的牛顿-拉夫逊（NR）求解器，尤其是在需要评估数千种场景的情况下。然而，当前PIGNN在同等速度下仍需提升精度；特别是，物理损失的软约束在推理阶段无效，这可能阻碍其在运行中的实际应用。我们通过PIGNN-Attn-LS来解决这一问题，该方法结合了边感知注意力机制与基于回溯线搜索的全局化校正算子。该注意力机制通过每条边的偏置显式编码线路物理特性，在计算图内部形成一个完全可微的已知算子层；而校正算子在推理阶段恢复了有效的下降准则。训练与测试使用了一个真实的高压/中压场景生成器，其中NR仅用于构建参考状态。在由4至32节点电网构成的保留高压测试案例中，PIGNN-Attn-LS在电压和相角上分别取得了0.00033标幺值和0.08度的测试均方根误差，较PIGNN-MLP基线分别提升了99.5%和87.1%。通过流式微批次处理，其在4至1024节点电网上的批量推理速度比NR快2至5倍。