No abstract

In this paper, extended-Kalman-filter-based estimation algorithms that could be used in combination with the speed-sensorless field-oriented control and direct-torque control of induction motors (IMs) are developed and implemented experimentally. The algorithms are designed aiming minimum estimation error in both transient and steady state over a wide velocity range, including very low and persistent zero-speed operation. A major challenge at very low and zero speed is the lost coupling effect from the rotor to the stator, which makes the information on rotor variables unobservable on the stator side. As a solution to this problem, in this paper, the load torque and the rotor angular velocity are simultaneously estimated, with the velocity taken into consideration via the equation of motion and not as a constant parameter, which is commonly the case in most past studies. The estimation of load torque, on the other hand, is performed as a constant parameter to account for Coulomb and viscous friction at steady state to improve the estimation performance at very low and zero speed. The estimation algorithms developed based on the rotor and stator fluxes are experimentally tested under challenging variations and reversals of the velocity and load torque (step-type and varying linearly with velocity) over a wide velocity range and at zero speed. In all the scenarios, the current estimation error has remained within a very narrow error band, also yielding acceptable velocity estimation errors, which motivate the use of the developed estimation method in sensorless control of IMs over a wide velocity range and persistent zero-speed operation.Index Terms-Extended Kalman filter (EKF), induction motor (IM), low/zero-speed operation, sensorless control.

No abstract

No abstract

No abstract

No abstract

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, isα and i sβ ; stator stationary axis components of the rotor flux, φrα and φ rβ ; rotor angular velocity, ωm ; stator resistance, Rs ; rotor resistance, Rr ; and load torque tL , as well as the magnetizing inductance, Lm , 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 Rs , Rr , tL , and Lm . The simulations confirm the effectiveness of the proposed BI-EKF-based estimator and, consequently, the speed-sensorless DVC of the IM.

No abstract