Study and investigation of parameter estimation algorithm for induction motor drive control using extended Kalman filter
Date of Issue2018-01-03
School of Electrical and Electronic Engineering
This thesis presents the parameter identification algorithm development for the induction motor drive control. Industrial applications demands for an electrical machine capable of operating with variable torque over fairly wide speed range. High performance control systems rely upon the parameter estimation. Estimation of machine parameters also helps for the purpose of machine diagnosis. A detailed literature review was done to understand the various concepts that include Electric drives and its main components such as Electric motors, power converters, controllers and sensors, various types of transformations such as three phase to two phase transformations etc., Also the concepts related to vector control are reviewed and understood the electrical behaviour of the asynchronous motor depends on the machine parameters which are in turn sensitive to the variations in temperature and saturation. Finally the steps involved to estimate electrical parameters using Extended Kalman Filter algorithm were also analysed. A detailed patent review was conducted to understand on various aspects of parameter identification for induction motor – offline parameter identification under standstill condition and online parameter identification. The review will aid to understand prevailing approaches for parameter estimation, recent advancements being used for parameter identifications. This will also help to understand existing patented methods and avoid the infringement of other companies’ intellectual properties. In this thesis an online parameter estimation algorithm using Extended Kalman filter is developed and simulated to determine the electrical parameters Rotor Resistance (Rr), Rotor leakage inductance (Llr), Stator leakage Inductance (Lls) effectively to prevent the mismatch between the controller parameters and actual motor parameters and thereby preventing the degradation in the performance of the electric drive. Also the importance of proper tuning of covariance and need for optimal initialization demonstrated.
DRNTU::Engineering::Electrical and electronic engineering