Online estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter
Recep Yıldız, Rıdvan Demir, Murat Barut
Abstract
In this study, a novel extended Kalman filter (EKF)-based observer is designed to increase the number of estimated states and parameters of the induction motor (IM). To perform the online estimations of stationary axis components of stator currents and rotor fluxes ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) as well as rotor mechanical speed ([Formula: see text]), which are required for direct vector control (DVC) systems along with the load torque ([Formula: see text]), rotor resistance ([Formula: see text]), magnetizing inductance ([Formula: see text]), and the reciprocal of the total inertia of the system ([Formula: see text]), the proposed EKF uses the measured phase currents and voltages together with the measured rotor speed. To estimate all of the five states ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) plus four parameters ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]), the proposed EKF-based observer does not include a switching operation nor a hybrid structure, which is a common approach in the literature for online state and parameter estimations of IMs and results in design complexity and computational load increase. In simulation studies, the estimation performance of the proposed EKF is tested and verified under the variations of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] in DVC systems that perform the speed and position controls of IM. The obtained results confirm the satisfying tracking performances and thus better control achievements of the speed and position controlled IM drives in this paper. Moreover, the proposed EKF and the EKF without [Formula: see text]-estimation are compared in the position control system to demonstrate the importance of the [Formula: see text] estimation. In the comparison, nearly 10 times less mean square error (MSE) is obtained in the estimations of [Formula: see text], [Formula: see text], [Formula: see text], and the magnitude of the rotor flux for the proposed EKF. Finally, the proposed EKF algorithm is tested and verified in real-time experiments with a challenging speed reversal scenario causing nonlinear variations in both [Formula: see text] and [Formula: see text].