PARTICLE SWARM OPTIMIZATION OF AN EXTENDED KALMAN FILTER FOR SPEED ESTIMATION OF AN INDUCTION MOTOR

Abstract

Induction motors have always been and will be the forefront of the industrial process. From construction to automated and electric vehicles, induction motors are the first choice. The rotor speed is an essential property in the efficiency of an induction motor which can be measured directly through an optical encoder which is placed on the motor shaft. However, the system cost, volume and weight of the motor are increased, moreover, the reliability and efficiency of the sensor are jeopardized in harsh environments. This paper proposes a method using an Extended Kalman Filter (EKF) to estimate the rotor speed with or without a sensor while reducing the noise created by a harsh environment. Furthermore, with the implementation of the Particle Swarm Optimization algorithm (PSO), a more accurate estimation has been achieved. Simulation in MATLAB Simulink has been done to further emphasize on the efficiency of the EKF-PSO framework