Hybrid DNN and EnKF framework for dynamic state estimation in power systems
TL;DR
A deep neural network-based dynamic SE framework with physics-constrained and ensemble Kalman filter-guided training that jointly incorporates prior-state supervision, EnKF-based covariance-aware correction, and nonlinear AC measurement consistency is proposed.
Abstract
As modern power systems evolve, their structural and operational characteristics are becoming increasingly dynamic, complex, and uncertain. Traditional model-driven state estimation (SE) methods face limitations in accuracy and adaptability under nonlinear and time-varying operating conditions. To address these challenges, this paper proposes a deep neural network (DNN)-based dynamic SE framework with physics-constrained and ensemble Kalman filter (EnKF)-guided training. The DNN is first pretrained using labeled measurement–state samples and is then refined through a second-stage offline optimization that jointly incorporates prior-state supervision, EnKF-based covariance-aware correction, and nonlinear AC measurement consistency. After offline training, direct DNN inference is combined with a physics-consistency-based residual gate for selective online adaptation. Simulation studies on the IEEE 118-bus system show that the proposed method achieves an mean absolute error of 0.0100 and an root mean square error of 0.0139, corresponding to reductions of 34.34% and 34.39%, respectively, compared with weighted least squares. Additional tests under impulsive noise and time-varying operating conditions further demonstrate the robustness and adaptability of the proposed framework.