A Square Root Cubature Kalman Filter Based Dynamic State Estimation of Distribution Network

As PMUs are gradually deployed in distribution network systems, power system dynamic state estimation (PSDSE) has gradually become an important research topic in intelligent distribution networks. The CKF algorithm is improved, in order to solve the problem that the square root of the covariance matrix needs to be calculated multiple times during the iteration of the cubature Kalman filter (CKF) algorithm, which result in rounding errors. We introduce square root filtering (SRF) to improve numerical stability. A power system dynamic state estimation method based on square root cubature Kalman filtering (SRCKF) is proposed, and algorithm steps are given. Finally, a simulation is performed on the IEEE30 node system to compare the estimated performance of the three algorithms the unscented Kalman filter (UKF), the CKF and the SRCKF. It is proved that the dynamic state estimation effect of the distribution network based on the cubature Kalman filter is superior to the UKF method. At the same time, the SRCKF algorithm proposed in this paper is superior to the CKF and UKF algorithms in terms of filtering accuracy and numerical stability.