Abstract In this thesis, two methods are proposed to detect stator winding fault in induction motors. The first method is based on a model based approach and a robust observer is presented to detect stator winding faults despite the load variations. Since the proposed observer should be robust, Unknown Input Observer (UIO) is used as robust observer. In the UIO design procedure, for flexible design, the stator currents and speed of induction motor are assumed as measurement signals. After designing a nonlinear UIO, an Extended Kalman Filter (EKF) is used for speed estimation of the induction motor. Therefore, speed measurement is relieved. Simulation results of stator winding fault with various short-inter-turns are shown to evaluate ability of the proposed approach. Finally, an experimental study on a 5-hp induction motor is performed under simulated short-turn stator winding. The results verify that faults can be reliably detected, and hence the proposed method can be used to improve safety and efficiency of the motors in industrial plants. In the second method, a Wiener Filter method has been proposed for detecting stator winding fault in induction motors. This filter is used for estimating and removing useless components from stator currents in order to detect stator winding fault with high reliability. Since the Park’s vector modulus is sensitive to abnormality in three-phase stator currents, it will be used for detection purposes. Finally, the validation of proposed method will be verified by experimental data from a 5-horespower induction motor. |
