用物理定律增强神经网络，提升电网状态估计的精度与抗攻击能力。
Robust Power System State Estimation using Physics-Informed Neural Networks
- 将电网物理规律嵌入神经网络，约束预测结果符合实际运行机制。
- 在未见数据上准确率比传统模型高83%，新数据集上性能提升65%。
- 对数据篡改攻击有强防御力，关键节点估计误差降低93%。
现代电力系统在状态估计与实时监控方面面临严峻挑战，尤其是在故障或网络攻击下的响应速度与准确性问题。本文提出一种基于物理信息神经网络（PINNs）的混合方法，通过将物理定律嵌入神经网络结构，提升输电系统在正常与故障条件下的状态估计精度与鲁棒性，同时展现应对数据篡改攻击的潜力。实验表明，该方法优于传统机器学习模型：在训练数据的未见子集上准确率最高提升83%，在全新且无关的数据集上性能提升65%；在针对系统关键母线的数据篡改攻击下，其估计精度比等效神经网络高出93%。
原文摘要 · Abstract (English)
Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This paper proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness, of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal and faulty conditions, while also showing potential in addressing security concerns such as data manipulation attacks. Experimental results show that the proposed approach outperforms traditional machine learning models, achieving up to 83% higher accuracy on unseen subsets of the training dataset and 65% better performance on entirely new, unrelated datasets. Experiments also show that during a data manipulation attack against a critical bus in a system, the PINN can be up to 93% more accurate than an equivalent neural network.
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