Model-based stator interturn short-circuit fault detection and diagnosis in induction motors

In this paper, a novel model-based method for induction motor with stator inter-turn short-circuit fault detection is presented. The proposed technique is based on the whiteness of innovation sequence developed by the standard extended Kalman filter. Nonlinear Generalized Likelihood Ratio method is applied to identify the faulty phase along with its severity. This technique just requires current sensors which are available in most induction motor drive systems to provide good controllability, and induction motor design details are not necessary. Computer simulations are carried out for a 4-hp squirrel cage induction motor using MATLAB environment. The results show the superiority of the proposed method as it provides better estimates for stator interturn fault detection.