Open AccessExtended Kalman filter based estimations for improving speed‐sensored control performance of induction motors
Author(s) -
Yildiz Recep,
Barut Murat,
Demir Ridvan
Publication year - 2020
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/iet-epa.2020.0319
Subject(s) - extended kalman filter , control theory (sociology) , stator , rotor (electric) , kalman filter , inductance , torque , induction motor , invariant extended kalman filter , engineering , computer science , control (management) , artificial intelligence , physics , mechanical engineering , electrical engineering , voltage , thermodynamics
In this study, an extended Kalman filter (EKF)‐based estimation algorithm is presented to improve the speed‐sensored control performance of induction motors (IMs). The proposed EKF‐based estimation algorithm is to simultaneously estimate the stator stationary axis components of stator currents and rotor fluxes, rotor angular speed, load torque including viscous friction term, rotor resistance and magnetising inductance in a single EKF algorithm without requiring any switching operation or a hybrid structure. In order to improve the speed‐sensored control performance, the measurement/output matrix of IM model is extended by the measured rotor speed in addition to stationary axis components of the measured stator currents. Therefore, the proposed EKF algorithm uses the speed and stator current errors between the measured and priori estimation values in order to calculate the posterior estimation ones. For performance evaluation, the eighth order (proposed) EKF algorithm is tested by simulations and real‐time experiments under challenging scenarios and compared with the developed sixth order EKF in real time. The obtained real‐time results also show that the eighth order (proposed) EKF algorithm provides additional and improved estimations with the increased but feasible execution time in terms of the sixth order EKF designed in this paper.