Speed estimation of an induction motor drive using an optimized extended Kalman filter

Abstract

Abstract-This paper presents a novel method to achieve good performance of an extended Kalman filter (EKF) for speed estimation of an induction motor drive. A real-coded genetic algorithm (GA) is used to optimize the noise covariance and weight matrices of the EKF, thereby ensuring filter stability and accuracy in speed estimation. Simulation studies on a constant V/Hz controller and a field-oriented controller (FOC) under various operating conditions demonstrate the efficacy of the proposed method. The experimental system consists of a prototype digital-signal-processor-based FOC induction motor drive with hardware facilities for acquiring the speed, voltage, and current signals to a PC. Experiments comprising offline GA training and verification phases are presented to validate the performance of the optimized EKF.

A new approach for speed estimation in induction motor drives based on a reduced-order extended Kalman filter

“…On the other hand, the speed scaling as in (6) gives another advantage of practical interest: although with different dynamics, the state variables have approximately the same magnitude and the tuning of the diagonal elements of the system covariance matrix become similar. Moreover, since the two components of the rotor flux have the same magnitude and dynamics, only two elements of the system covariance matrix (Q) must be tuned:…”

“…Consequently, on one hand the computational effort is smaller and, on the other hand, the complexity is reduced since the tuning of the algorithm becomes simpler, mainly due to the lower dimension of the state vector. Thus, the genetic algorithm proposed in [6] is not needed any more. Furthermore, if needed, a 2 nd -order approximation becomes practicable and no more fastidious, with the particular discretization process proposed by the authors in this work.…”

“…A recent effort in research on sensorless drive control of standard induction motors has been the estimation of rotor speed from the measurement of stator voltages and currents using either the MRAS [4], [8] or the EKF [6], [7] and [9]. This work deals with a new approach for real-time estimation of mechanical speed and rotor flux components by using a reduced-order EKF.…”

“…This work deals with a new approach for real-time estimation of mechanical speed and rotor flux components by using a reduced-order EKF. Rather than a full-order EKF for speed estimation that also estimates stator current components, besides the estimation of rotor flux components [6], [7], rotor current components [9] or magnetizing current [10], an EKF algorithm based on a reduced-order statespace model is proposed in this paper. In this case only the rotor flux dq components are estimated, besides the rotor speed itself.…”

“…Speed estimation methods are being used that avoid the speed measurement set-up and commercial sensorless vector-controlled drives are already available. There are several speed estimation techniques in the literature which are generally classified in [3] as follows: slip calculation; direct synthesis from state equations; model reference adaptive system (MRAS) [4]; speed adaptive flux observer; slot harmonics; injection of auxiliary signal on salient rotor to improve the estimation at low speed [5] and extended Kalman filter (EKF) based methods, [6], [7].…”