SPEED ESTIMATION USING EXTENDED KALMAN FILTER TECHNIQUE
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Abstract
This paper presents a state estimation technique for speed sensorless field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations using MATLAB package VER.6.3.
A stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis stator currents. Thus, using this observer the rotor speed and rotor fluxes are estimated simultaneously. Different from the widely accepted use of EKF, in which it is optimized for either steady- state or transient operations, here using adjustable noise level process algorithm the optimization of EKF has been done for both states; the steady-state and the transient-state of operations.
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