This work proposes an artificial neural network supported method to establish an automatic detection of stator turn fault in induction motor. The sequence current analysis is done for turn fault condition. Various factors influencing the total measured negative sequence current such as unbalanced voltage and inherent asymmetry have been reviewed. To compensate the voltage unbalance and non-idealities in the machine, utilization of measured negative sequence current, impedance, admittance, or semi empirical formula is developed. The output of a well-trained feed forward back propagation neural network classifies the severity of fault level in stator winding. The method of considering the effects of turn faults on inter-turn fault detection improves sensitivity meanwhile reduces the prospect of misdetection.

这项工作提出了一种人工神经网络支持的方法来建立感应电动机中定子转动故障的自动检测。针对转向故障条件进行了顺序电流分析。综述了影响总的负序电流的各种因素，例如不平衡电压和固有的不对称性。为了补偿电机中的电压不平衡和不理想情况，开发了利用测得的负序电流，阻抗，导纳或半经验公式。训练有素的前馈反馈神经网络的输出可对定子绕组中故障等级的严重程度进行分类。考虑匝间故障对匝间故障检测的影响的方法提高了灵敏度，同时减少了误检测的可能性。