On-Line Parameters Estimation of Scale SPSG Using Discrete Kalman Filters

Parametric identification techniques are applied in year’s space from electrical machines. Many of these techniques are verging on each other in this field. In fact, certain techniques are more adapted to a recorded time parametric identification and which are called ‘off-line methods’. The off-line me other techniques are more suitable for a real time estimation of the parameters and are known as ‘on-line methods’. The on-line methods concern mainly the case where the machine is functioning under load conditions, but both methods (off optimization algorithm to minimize the error between the real an this article, the study was carried out on a salient KW. At first, the performance of experimental parametric identification using off presented; subsequently, different estimators were applied to on The discrete Kalman filter (DKF) is the estimator applied in this its traditional form (DTKF) for linear systems or in its extended form (DEKF) when the system is nonlinear. Another attractive application of the DKF is when it is biased (DBEKF). The consideration of the bias makes it possible between measured and estimated values of the system state variable; accordingly, the normalized MSE (NMSE) can be minimized. Likewise, standard deviation (STD) between real and estimated values of the parameter can be discussed and the different DKFs are implemented in Matlab/Simulink code in order to demonstrate the effectiveness of DBEKF estimator compared to the other filter simulation results are satisfying since good agreement between real and estimated parameters was obtained which means an acceptable noise filtering quality of the designed Kalman estimators. All estimators can be used in on for low scale generato