用物理约束神经网络精准求解电力系统潮流，效果远超传统方法。
Adaptive Informed Deep Neural Networks for Power Flow Analysis
- 双头网络结合功率注入特征自适应激活函数
- 物理损失函数融合电网拓扑信息，提升预测精度
- 在4至2224节点系统上表现优异，适合大规模电网应用
本研究提出PINN4PF，一种端到端深度学习架构，用于电力系统潮流（PF）分析，能有效捕捉现代大型电力系统的非线性动态。该神经网络架构在训练流程中包含两项关键改进：(A) 双头前馈网络，其激活函数根据净有功与无功注入模式自适应调整；(B) 基于物理的损失函数，通过新颖的隐含函数部分引入电网拓扑信息。在4、15、290和2224节点测试系统上验证了该架构的有效性，并与线性回归模型（LR）和黑箱神经网络（MLP）进行对比。评估维度包括泛化能力、鲁棒性、训练数据量对泛化的影响、导出物理量（线路电流、有功功率、无功功率）的逼近精度以及可扩展性。结果表明，无论在何种测试系统中，PINN4PF均优于两个基线模型，性能提升最高达两个数量级，不仅在直接指标上表现更优，且在衍生物理量逼近方面也显著领先。
原文摘要 · Abstract (English)
This study introduces PINN4PF, an end-to-end deep learning architecture for power flow (PF) analysis that effectively captures the nonlinear dynamics of large-scale modern power systems. The proposed neural network (NN) architecture consists of two important advancements in the training pipeline: (A) a double-head feed-forward NN that aligns with PF analysis, including an activation function that adjusts to the net active and reactive power injections patterns, and (B) a physics-based loss function that partially incorporates power system topology information through a novel hidden function. The effectiveness of the proposed architecture is illustrated through 4-bus, 15-bus, 290-bus, and 2224-bus test systems and is evaluated against two baselines: a linear regression model (LR) and a black-box NN (MLP). The comparison is based on (i) generalization ability, (ii) robustness, (iii) impact of training dataset size on generalization ability, (iv) accuracy in approximating derived PF quantities (specifically line current, line active power, and line reactive power), and (v) scalability. Results demonstrate that PINN4PF outperforms both baselines across all test systems by up to two orders of magnitude not only in terms of direct criteria, e.g., generalization ability, but also in terms of approximating derived physical quantities.
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