Turkish Journal of Electrical Engineering and Computer Sciences




This study introduces a novel bi input-extended Kalman filter (BI-EKF)-based speed-sensorless direct vector control (DVC) of an induction motor (IM). The proposed BI-EKF-based estimator includes online estimations of the stator stationary axis components of the stator currents, i_{s\alpha} and i_{s\beta}; stator stationary axis components of the rotor flux, \varphi_{r\alpha} and \varphi_{r\beta}; rotor angular velocity, \omega_m; stator resistance, R_s; rotor resistance, R_r; and load torque t_L, as well as the magnetizing inductance, L_m, by only supposing that the stator phase currents and voltages are measured. Thus, the speed-sensorless DVC of the IM with the inclusion of the proposed estimator is able to be perfectly operated at a wide speed range, varying from zero speed to beyond the rated/based speed under the extreme variations in R_s, R_r, t_L, and L_m . The simulations confirm the effectiveness of the proposed BI-EKF-based estimator and, consequently, the speed-sensorless DVC of the IM.


Induction motor, extended Kalman filter, rotor-stator resistance estimation, load torque estimation, magnetizing inductance estimation, sensorless control

First Page


Last Page