Discrimination of stator winding turn fault and unbalanced supply voltage in permanent magnet synchronous motor using ANN

Abstract

Permanent magnet synchronous motor (PMSM) is currently the most attractive application electric machine for several industrial applications. It has obtained widespread application in motor drives in recent time. However, different types of faults are unavoidable in such motors. This paper focuses on stator winding faults diagnosis. This paper proposes the ratio of third harmonic to fundamental FFT magnitude component of the three-phase stator line current and supply voltage as a parameter for detecting stator winding turn faults under different load conditions and using artificial neural network (ANN). Discrimination among unbalancing of supply voltage conditions and stator turn short circuit poses a challenge that is addressed in this paper. The presented approach yields a high degree of accuracy in fault detection and diagnosis between the effects of stator winding turn fault and those due to unbalanced supply voltages using artificial neural network. All simulations in this paper are conducted using finite element analysis software.