Induction motor state estimation using tuned Extended Kalman Filter

As a main limitation in the state and parameters estimation using Extended Kalman Filter (EKF) is that its optimality is critically dependent on the choice of the right covariance matrices of state and measurement noise. In order to overcome this difficulty, a new approach based on the use of the tuned EKF to estimate simultaneously the speed and rotor flux of an induction motor drive is proposed. This approach will firstly optimize the covariance matrices by the Particle Swarm Optimization (PSO) algorithm and after that, the values of these covariance matrices are introduced in the estimation loop. Computer simulation results indicate an accurate estimation and an acceptable performance in speed-rotor flux estimation after considerable tuning of the covariance matrices coefficients and confirm the efficiency of our proposed method.