Abstract
With the emergence of sensorless control methods, there was a need for the use of estimators and/or state observers to give it the robustness and precision required in the drive of induction motors. This work deals with the application of the Extended Kalman Filter (EKF) in the estimation of rotor speed and position, aiming at the implementation of the indirect vector control technique in a sensorless speed control system for three-phase induction motors. The mathematical development of the system state variables associated with the EKF stochastic process is presented in this study, and point out its application under variable speed and load conditions, which are imposed on these motors in everyday life. The sensorless control strategy was tested through routine lines in the Matlab® software, simulating operating conditions of this type of engine, being proven its performance, as well as the convergence times consistent with the usual requirements of high performance systems. The main contributions of this work are the use of a reduced-order EKF (ROEKF) and the preset of covariance matrices to accelerate convergence in speed and position estimates for future implementations in currently accessible digital signal processors.
Highlights
In recent decades, the applications of alternating current (AC) drive systems, associated with vector control techniques with speed and/or position monitoring by means of electromechanical sensors, have established themselves as a low cost alternative, greater mechanical robustness and high performance, equating to the CC drives precursors
SIMULATION RESULTS The simulations were performed in MatLab®, considering the control in open mesh and in closed mesh using the values estimated by Extended Kalman Filter (EKF)
An Extended Kalman Filter, with pre-adjusted covariance matrices, applied to the sensorless speed control of three-phase induction motors acts less intensely to minimize the discrepancy between the prediction values and the measures taken on the machine model
Summary
The applications of alternating current (AC) drive systems, associated with vector control techniques with speed and/or position monitoring by means of electromechanical sensors, have established themselves as a low cost alternative, greater mechanical robustness and high performance, equating to the CC drives precursors. The Bi-EKF is based on two mathematical models, one for the stator flow and the other for the rotor flow in the stationary frame, providing the complete estimation of all induction motor states in conjunction with the stator and rotor resistances, in a wide speed range (BARUT et al, 2012) Another method derived from EKF is the Reduced-Order Extended Kalman Filter (ROEKF) which, with fewer variables, provides the reduction of processing time and estimation errors without compromising system performance (YIN et al, 2013) (LEITE et al, 2004). Aiming at reducing processing time and future implementation using the Texas Instrument Manufacturer’s digital signal processor, model TMDSHVMTRPFCKIT, an alternative to the 6th and 7th orders EKFs usually applied that include stator current components, rotor electromagnetic flows and stator and rotor resistances (DADKHAH et al, 2015) (BOGOSYAN and GOKASAN, 2007) (BARUT, 2010) (BARUT et al, 2012). The main contributions of this work refer to the ROEKF analyses in relation to the accuracy and convergence of the speed estimation under different operating and load conditions as well as the adaptation, development and testing of the techniques of off-line tuning of the covariance matrices of the modeling errors and measurements
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