Mixture correntropy unscented Kalman filter for power system dynamic state estimation

Abstract

Unscented Kalman filter (UKF) based on correntropy criterion shows robustness when power system measurement suffers from non-Gaussian noise. To improve the performance of traditional algorithms, this paper proposed a generalized mixture correntropy unscented Kalman filter (GMC-UKF) for power system dynamic state estimation. Specifically, we construct the mixture correntropy by two generalized Gaussian kernels. After introducing the weighted state error and measurement error into the mixture correntropy cost function, we adopt fixed-point iteration to obtain optimal estimation. Finally, the robustness and accuracy of the proposed algorithm for power system state estimation are verified on IEEE-30bus.