ANN-based diagnosis of incipient stator winding turn faults for three-phase induction motors in the presence of unbalanced supply voltage

Perfectly balanced supply voltages are not possible in practice. Therefore, detection, discrimination and diagnosis of stator winding turn fault in the presence of unbalanced supply voltages for three-phase induction motors is needed. In this paper a novel approach is presented for stator winding turn incipient faults detection in the presence of different levels of voltage unbalance and at different load conditions. The proposed method investigates and utilizes the ratio between third harmonic and fundamental voltage and current waveform. Fast Fourier Transform (FFT) magnitude components of the stator currents and voltages are utilized for detection and estimation of different insulation failure percentages in the presence of unbalanced supply voltages. The method uses artificial neural networks (ANN) and is tested through simulation and experimental investigations. The proposed approach presents a high degree of accuracy in detection and diagnosis of stator winding turn faults in the presence of unbalanced supply voltages condition. The method discriminates between the effect of incipient stator winding turn fault and those due to unbalanced supply voltage. In addition, the proposed approach gives a more significant and reliable indicator for detection and diagnosis of stator winding turn faults in the presence of unbalanced supply voltages conditions.