Square Root CKF based on Maximum Mixture Correntropy with Generalized Versoria for Power System State Estimation

In this paper, a square root cubature Kalman filter (CKF) based on maximum mixture correntropy criterion with generalized Versoria kernel (GV-MMC-SRCKF) is proposed to overcome the influence of non-Gaussian noise and outliers in power system state estimation. Specifically, two generalized Versoria kernel functions with different shape coefficients are merged in an optimization criterion that replaces the traditional mean square error criterion. In addition, the proposed algorithm also solves the limitations of CKF and the basic maximum correntropy criterion. The results of estimating the state of the 30,57-bus power system under complex conditions have confirmed the high performance of the proposed algorithm.