Zerdali, Emrah2024-11-072024-11-072019978-1-5386-8086-5https://hdl.handle.net/11480/137771st Global Power, Energy and Communication Conference (IEEE GPECOM) -- JUN 12-15, 2019 -- Nevsehir, TURKEYIn this paper, a strong tracking extended Kalman filter (STEKF) algorithm estimating the stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, and load torque is proposed for speed-sensorless control applications of induction motor. As known, Kalman filtering requires complete specifications of both dynamical and statistical model parameters of a system to achieve optimal performance. Therefore, the proper determination of system and measurement noise covariance matrices is crucial. However, these matrices are generally assumed as constant and determined by trial-and-error method. Failure to find optimum values by trial-and-error method for all operating conditions and the variation of these matrices according to operating conditions cause the divergence of algorithm or deterioration of its performance. Therefore, a sixth-order STEKF algorithm improving the estimation performance is designed and tested in simulations. Moreover, its performance is compared to that of standard EKF in order to prove its superiority.eninfo:eu-repo/semantics/closedAccessInduction motorSpeed-sensorless controlState estimationStrong tracking extended Kalman filterConference Object2162212-s2.0-85070656684N/AWOS:000851517900040N/A