Model-based fault detection in induction Motors

In this paper a model-based fault detection method for induction Motors is presented. A new filtering technique based on Unscented Kalman filters and Extended Kalman filters, is utilized as a state estimation tool in broken bars detection of induction motors. Using the merits of these recent nonlinear estimation tools UKF and EKF, rotor resistance of an induction motor is estimated only by the sensed stator currents and voltages information. In order to compare the estimation performances of EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. The results show the superiorly of UKF over EKF in highly nonlinear systems, as it provides better estimates of which is most critical for rotor fault detection.