用物理约束提升电网潮流预测的可靠性与速度
KCLNet: Physics-Informed Power Flow Prediction via Constraints Projections
- 通过超平面投影将基尔霍夫定律作为硬约束融入图神经网络
- 预测精度媲美传统方法，且零违反基尔霍夫电流定律
- 适合需要高物理一致性电网仿真与实时控制的场景
在现代电力系统中，快速、可扩展且符合物理规律的潮流预测对保障电网安全高效运行至关重要。传统数值方法虽稳健，但在动态或故障条件下需大量计算以维持物理一致性。近年来人工智能显著提升了计算速度，但常无法在真实故障场景中遵守基本物理定律，导致预测结果不物理。本文提出KCLNet，一种基于图神经网络的物理信息模型，通过超平面投影将基尔霍夫电流定律（KCL）作为硬约束引入。KCLNet在保持竞争性预测精度的同时，实现零KCL违规，确保了现代智能电网运行所需的可靠与物理一致的潮流预测。
原文摘要 · Abstract (English)
In the modern context of power systems, rapid, scalable, and physically plausible power flow predictions are essential for ensuring the grid's safe and efficient operation. While traditional numerical methods have proven robust, they require extensive computation to maintain physical fidelity under dynamic or contingency conditions. In contrast, recent advancements in artificial intelligence (AI) have significantly improved computational speed; however, they often fail to enforce fundamental physical laws during real-world contingencies, resulting in physically implausible predictions. In this work, we introduce KCLNet, a physics-informed graph neural network that incorporates Kirchhoff's Current Law as a hard constraint via hyperplane projections. KCLNet attains competitive prediction accuracy while ensuring zero KCL violations, thereby delivering reliable and physically consistent power flow predictions critical to secure the operation of modern smart grids.
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