摘要
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.