On-line estimation of SPSG parameters using discrete Kalman filters

Diverse estimators are available for on-line parametric identification; among them the discrete Kalman filter (DKF) is the most popular. It can be used in its traditional form (DTKF) for linear systems or in its extended form (DEKF) when the system is nonlinear. Another interesting application of the discrete Kalman filter is when it is biased (DBKF). The consideration of the bias makes it possible to reduce the mean squared error (MSE) between measured and estimated values of the system state variable. Therefore the normalized MSE (NMSE) can be diminished as well. Likewise, standard deviation (STD) between real and estimated values of the parameter can be limited in the tolerable percentage. All these situations are discussed in this paper where the system under study is a salient-pole synchronous generator (SPSG). MATLAB codes and Simulink models are implemented to validate the different DKFs. Finally comparative study between continuous and discrete KFs is provided and which reveals the benefit of the DKF.