High performance parameter observation of induction motor with sensorless vector Control using extended Kalman filter

Speed sensor less vector control of induction motor drive faces two major problems: speed estimation, and rotor flux observation. Because of the multiplication terms of state variables, the induction motor model is consisted of nonlinear state equations. To estimate the state variables of the motor model and gain the rotor flux and speed signals, a method is proposed in this paper using extended Kalman filter. Software programs are used to carry out extended Kalman filter (EKF) algorithm to estimate the rotor speed and fluxes. The obtained results prove that extended Kalman filter algorithm can estimate rotor speed and flux very accurately, and based on that, the speed sensor less drive system can have good static and dynamic performance.