Power System State Estimation Based on RWUKF Algorithm

A robust weighted unscented Kalman filter(RWUKF) algorithm is proposed to solve the problem that the unscented Kalman filter(UKF) algorithm is easily affected by system noises and gross errors in power system state estimation. By modifying Sage-Husa noise statistic estimator, the stability of noise statistics is improved and the robustness of the algorithm is guaranteed. In addition, the estimation index based on Markov distance is introduced, and the measurement noise error matrix is modified by adaptive weighting factor, which reduces the influence of gross error on filtering accuracy. Simulation results of IEEE 30-bus standard system show that the proposed RWUKF algorithm is better than the traditional UKF algorithm.