Negative Sequence Current Compensation for Induction Motor Stator Inter-Turn Short Circuit and Off-Diagonal Term in Sequence Impedance Matrix as Fault Indicator

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.