State Estimation of Induction Motors Using Particle Swarm Optimization Tuned Unscented Kalman Filter

This paper deals with sensorless speed estimation of an Induction Motor with minimum mean square error. For sensorless speed estimation, Unscented Kalman Filter (UKF) is used. The performance of this filter depends on the process and the measurement noise covariance parameters of the estimator which estimates stator and rotor current, rotor flux, rotor speed, torque and it is also influenced by three scalar parameters such as alpha, beta, and kappa. As the selection of these values are not of a straight forward approach, an optimization algorithm such as Particle Swarm Optimization (PSO) is used to attain minimum error. The result shows that the filter performance is improved by using PSO tuning and gives optimized minimum error compared to conventional UKF under various machine operating conditions.