Dynamic Harmonic State Estimation of Power System Based on Sage-Husa Square-Root Unscented Kalman Filter

The limitations and shortcomings of traditional Kalman filter (KF) and its derivative algorithms with the application of harmonic state estimation (HSE) are analyzed. With the consideration that the power system is nonlinear and the measurement noise is an unknown random noise which is difficult to estimate by experience or hypothesis, an improved Sage-Husa square-root unscented KF (SH-SRUKF) algorithm based on SRUKF is introduced and derived, the method and flowchart for dynamic HSE under SH-SRUKF algorithm is proposed in this paper. Taking an IEEE14 test system as an example, the results of dynamic HSE in three scenarios are discussed, including the measurement noise is uncertain random noise, it might contain bad data and feasible fluctuations in load power. The simulation results indicate that SH-SRUKF performs better than SRUKF in the robustness and accuracy for dynamic HSE.