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Rajamany Gayatridevi,Rajamany Krishnan,Natarajan Ramesh K. 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.4
This work proposes an artifi cial 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 infl uencing 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 classifi es the severity of fault level in stator winding. The method of considering the eff ects of turn faults on inter-turn fault detection improves sensitivity meanwhile reduces the prospect of misdetection.
Gayatridevi Rajamany,Sekar Srinivasan 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.6
This paper deals with artificial neural network approach for automatic detection of severity level of stator winding fault in induction motor. The problem is faced through modelling and simulation of induction motor with inter coil shorting in stator winding. The sum of the absolute values of difference in the peak values of phase currents from each half cycle has been chosen as the main input to the classifier. Sample values from workspace of Simulink model, which are verified with experiment setup practically, have been imported to neural network architecture. Consideration of a single input extracted from time domain simplifies and advances the fault detection technique. The output of the feed forward back propagation neural network classifies the short circuit fault level of the stator winding.
Rajamany, Gayatridevi,Srinivasan, Sekar The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.6
This paper deals with artificial neural network approach for automatic detection of severity level of stator winding fault in induction motor. The problem is faced through modelling and simulation of induction motor with inter coil shorting in stator winding. The sum of the absolute values of difference in the peak values of phase currents from each half cycle has been chosen as the main input to the classifier. Sample values from workspace of Simulink model, which are verified with experiment setup practically, have been imported to neural network architecture. Consideration of a single input extracted from time domain simplifies and advances the fault detection technique. The output of the feed forward back propagation neural network classifies the short circuit fault level of the stator winding.