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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.

This research discusses d-q model and Rotor FluxOriented Control (RFOC) technique for faulty three-phase Induction Motor (three-phase IM when one of the stator phases is opened). In the controlling technique, two transformation matrixes are applied to the equations of faulty three-phase IM. As a result, the equations of faulty three-phase IM become similar to the balanced IM. Therefore, by employing some modifications in the conventional block diagram of the balanced IM, faulty motor control is possible. Additionally, for high performance vector control of the faulty IM, an Extended Kalman Filter (EKF) is used for motor speed estimation. Simulation results demonstrate the validity and applicability of this technique to improve performance of the faulty IM.

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