Synchronous Generator Dual Estimation Using Sigma Points Kalman Filter

Abstract

In this article, the central difference Kalman filter (CDKF) has been used to estimate the parameters of two different models of synchronous generator (SG) in the presence of noise. It should be mentioned that there are different models of synchronous generators with different levels of accuracy for use in estimation algorithms. The estimation algorithm in this paper uses a smaller number of measurement inputs to estimate the states and unknown parameters for two exact models of the synchronous generator. The central difference Kalman filter (CDKF) is a member of the Kalman filter family, which, like the unscented Kalman filter (UKF), uses sigma points to model nonlinear equations. The differential Kalman filter (CDKF) provides better results than the unscented Kalman filter. In this research, by using two synchronous generator models with different parameters in three scenarios, the ability of the Kalman filter of the central difference is challenged, which shows that this method is very efficient and reliable.