Yazar "Zerdali E." seçeneğine göre listele

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    Adaptive Extended Kalman Filter for Speed-Sensorless Control of Induction Motors
    (Institute of Electrical and Electronics Engineers Inc., 2019) Zerdali E.
    This paper presents an adaptive extended Kalman filter (AEKF) algorithm estimating the stator stationary axis components of stator currents, the stator stationary axis components of rotor fluxes, the rotor mechanical speed, and the load torque for speed-sensorless control applications of induction motors (IMs). The performance of a standard extended Kalman filter (SEKF) algorithm depends on the correct selection of system and measurement noise covariance matrices. In SEKF algorithms, these matrices are generally assumed as constant and determined by the trial-and-error method. However, they are affected by the operating conditions of IM and should be updated considering the operating conditions. For this purpose, instead of the time-consuming trial-and-error method for determining these matrices, an innovation-based adaptive estimation approach having the capability of online update is used in this paper. Finally, in order to verify the superiority of the AEKF algorithm, estimation performances of AEKF and SEKF algorithms are compared under challenging scenarios for a wide speed range considering computational burdens and parameter variations. © 1986-2012 IEEE.
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    Adaptive Fading Extended Kalman Filter Based Speed-Sensorless Induction Motor Drive
    (Institute of Electrical and Electronics Engineers Inc., 2018) Zerdali E.; Yildiz R.; Inan R.; Demir R.; Barut M.
    This paper presents an adaptive fading extended Kalman filter (AFEKF) based speed-sensorless induction motor (IM) drive. Conventional extended Kalman filters (CEKFs) assume the system (Q) and the measurement (R) noise covariance matrices as constant, but those matrices are affected by the operating conditions of IMs and deteriorate the estimation performance. To eliminate this adverse effect, an AFEKF algorithm which has the ability to update Q and R matrices according to the operating conditions of IM are proposed, and the stator stationary axis components of stator currents, the stator stationary axis components of rotor fluxes, the rotor mechanical speed, and the load torque including viscous friction term are estimated. To illustrate the superiority of AFEKF-based speed-sensorless IM drive, the control performance of the proposed drive system is compared to that of CEKF-based speed-sensorless drive system under simulations. In addition to the comparison results, the computational burdens of AFEKF and CEKF algorithms are also examined. © 2018 IEEE.
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    Optimization of model reference adaptive system based speed estimation for speed sensorless control of induction motors via differential evolution algorithm
    (IFAC Secretariat, 2013) Barut M.; Yalcin M.; Zerdali E.; Demir R.
    This study proposes an optimally tuned Model Reference Adaptive System (MRAS) based speed estimator using back electromotive force (EMF) vector, which does not require pure integration. The PI (Proportional and Integral) gain coefficients in the speed estimator are optimally determined by utilizing Differential Evolution (DE) algorithm. The performance of the speed estimator is tested with both simulation and real-time experiments for a wide speed range. The obtained results verify the desired performance of speed estimation. © IFAC.