In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation. Traditionally, physics-based models are used including weighted least square (WLS) or weighted least absolute value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this paper, a physics-guided deep learning (PGDL) method is proposed. Specifically, inspired by autoencoders, deep neural networks (DNNs) are used to learn the temporal correlations. The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations. Hence, the proposed PGDL is both data-driven and physics-guided. The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases. Simulations show promising results and the applicability is further discussed.

中文翻译：

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物理指导的深度学习用于电力系统状态估计

在过去的十年中，机器学习领域取得了巨大的进步。本文探讨了将深度学习应用于电力系统状态估计的可能性。传统上，使用基于物理的模型，包括加权最小二乘（WLS）或加权最小绝对值（WLAV）。这些模型通常考虑系统的单个快照，而不捕获系统状态的时间相关性。本文提出了一种物理指导的深度学习（PGDL）方法。具体而言，受自动编码器的启发，深度神经网络（DNN）用于学习时间相关性。然后，通过运行一组潮流方程，对照物理定律检查来自DNN的估计系统状态。因此，提出的PGDL既受数据驱动又受物理学指导。在标准IEEE情况下，将所提出的PGDL方法的准确性和鲁棒性与传统方法进行了比较。仿真显示了令人鼓舞的结果，并进一步讨论了其适用性。