Using NSGA II Multiobjective Genetic Algorithm for EKF-based Estimation of Speed and Electrical Torque in AC Induction Machines

High-performance AC drives require accurate speed and torque estimations to provide a proper system operation. The selection and quality of extended Kalman fitter (EKF) covariance elements have a considerable bearing on the effectiveness of motor drive. Many EKF-based optimization techniques involve only a single objective for the optimal estimation of speed without giving concern to the torque. This paper presents a new methodology for the selection of EKF filters that uses non-dominated sorting genetic algorithm-II (NSGA-II) developed for filter element selection in order to investigate the concurrent optimization of speed and torque. The proposed optimizing technique for EKF-based estimation scheme is used in the combination with the sensorless direct torque control of induction motor. The multi-optimal based-EKF is tested with three regions of Pareto front curve.