Deep Reinforcement-Learning-Optimized Adaptive EKF for Robust Utility Harmonic Impedance Estimation

Accurate estimation of the utility harmonic impedance at the Point of Common Coupling (PCC) is critical for harmonic pollution management in industrial power grids. Existing non-invasive methods rely heavily on restrictive assumptions that are rarely satisfied in practice, and conventional filtering-based approaches suffer from accuracy degradation in dynamic scenarios due to fixed-rule updates of the noise covariance. This paper proposes a deep reinforcement learning (RL)-optimized adaptive extended Kalman filter (AEKF) method for robust harmonic impedance estimation. A state-space model is established without restrictive assumptions, and a deep Q-network (DQN) framework is designed to optimize noise covariance updates adaptively. Simulation results show that the method achieves reliable estimation under normal conditions. Although errors rise under strong noise, it remains stable and exhibits better noise robustness than conventional methods. Field measurements in actual power grid environments further verified the feasibility and application potential of the proposed method in field engineering.