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 active and reactive power consumption patterns, and (B) a physics-based loss function that partially incorporates power system topology information. 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.

翻译：本研究提出了PINN4PF，一种用于潮流分析的端到端深度学习架构，能有效捕捉大规模现代电力系统的非线性动态特性。所提出的神经网络架构在训练流程中包含两项重要改进：(A) 与潮流分析相匹配的双头前馈神经网络，包含可根据有功和无功功率消耗模式进行调节的激活函数；(B) 部分融合电力系统拓扑信息的物理信息损失函数。通过4节点、15节点、290节点和2224节点测试系统验证了所提架构的有效性，并与线性回归模型和黑盒神经网络两种基线模型进行了对比评估。评估基于以下五个维度：(i) 泛化能力，(ii) 鲁棒性，(iii) 训练数据集规模对泛化能力的影响，(iv) 派生潮流物理量（具体包括线路电流、线路有功功率和线路无功功率）的逼近精度，以及(v) 可扩展性。实验结果表明，在所有测试系统中，PINN4PF不仅在线性回归等直接评估指标上优于基线模型两个数量级，在派生物理量的逼近精度方面同样展现出显著优势。