The growing trend toward the modernization of power distribution systems has facilitated the installation of advanced measurement units and promotion of the cyber communication systems. However, these infrastructures are still prone to stealth cyber attacks. The existing data-driven anomaly detection methods suffer from a lack of knowledge about the system's physics, lack of interpretability, and scalability issues hindering their practical applications in real-world scenarios. To address these concerns, physics-informed neural networks (PINNs) were introduced. This paper proposes a multivariate physics-informed convolutional autoencoder (PIConvAE) to detect stealthy cyber-attacks in power distribution grids. The proposed model integrates the physical principles into the loss function of the neural network by applying Kirchhoff's law. Simulations are performed on the modified IEEE 13-bus and 123-bus systems using OpenDSS software to validate the efficacy of the proposed model for stealth attacks. The numerical results prove the superior performance of the proposed PIConvAE in three aspects: a) it provides more accurate results compared to the data-driven ConvAE model, b) it requires less training time to converge c) the model excels in effectively detecting a wide range of attack magnitudes making it powerful in detecting stealth attacks.

翻译：随着配电系统现代化趋势的推进，高级测量单元的安装与网络通信系统的推广得以加速。然而，这些基础设施仍易受隐蔽式网络攻击。现有数据驱动的异常检测方法存在对系统物理机理认知不足、可解释性缺失及可扩展性瓶颈等问题，制约了其在真实场景中的实际应用。为此，物理信息神经网络（PINNs）被引入相关研究。本文提出一种多变量物理信息卷积自编码器（PIConvAE），用于检测配电网中的隐蔽式网络攻击。该模型通过应用基尔霍夫定律，将物理原理融入神经网络的损失函数。采用OpenDSS软件在改进的IEEE 13节点和123节点测试系统上进行仿真，验证了所提模型对隐蔽攻击的有效性。数值结果表明，所提出的PIConvAE在三个方面展现出优越性能：a) 相较于数据驱动的ConvAE模型，其检测结果更精确；b) 收敛所需训练时间更短；c) 该模型能有效检测广泛攻击幅度的隐蔽攻击，在隐蔽攻击检测方面具有显著优势。