Improved Sage-Husa Unscented Kalman Filter for Harmonic State Estimation in Distribution Grid

In distribution grids with the large-scale integration of renewable energy sources and energy storage systems, power signals are often contaminated with time-varying noise and frequency deviation caused by low-frequency inertia. To achieve an accurate dynamic harmonic state estimate (HSE), a novel method based on an improved Sage-Husa unscented Kalman filter (ISHUKF) is proposed. Considering the frequency deviation, a nonlinear filter model for power signal is proposed, and a UKF is used to address the nonlinear estimation. A Sage-Husa noise estimator is incorporated to enhance the robustness of the UKF-based HSE against the time-varying noise. Additionally, the noise covariance of the Sage-Husa algorithm is modified to ensure the rapid convergence of the estimation. Then, the performance of the proposed method is validated using an IEEE 14-node system. Finally, the method is applied to evaluate the harmonic states of grid-connected inverter faults in real-world scenarios. The simulation and experiment results demonstrate that the proposed method provides an accurate dynamic HSE even in the presence of time-varying noise and frequency deviation.