ONLINE STATOR FAULT DETECTION OF INDUCTION MOTORS USING PARAMETER IDENTIFICATION TECHNIQUES

摘要

Induction motors are used widely in industry as prime power drivers. Small faults occurring in the motors cause deficient operation and influence normal production processes. To find the faults at their infant stage many advanced methods have been developed including vibro-acoustic spectrum analysis and motor current signature analysis. This paper studies parameter identification techniques to develop real time motor condition monitoring. There are 3 RLS algorithms: RLS with Efficient Matrix Inversion, RLS with Normalized Gain, and RLS with Multivariable Case. The algorithms are applied to identify motor parameters including stator resistance and stator leakage inductance. The phase voltage and current are used as measured data. They are acquired from both the simulated and actual induction motors. The model is validated on both simulation experimental studies. It shows that several common motor faults including loose electrical connections, short-circuits and imbalanced supply can be detected by checking the change in stator resistance. This method not only detects the faults but also quantify how much faults are happening in the induction motor.