Detection and discrimination of stator interturn fault and unbalanced supply voltage fault in induction motor using neural network

The three-phase shift between the line current and phase voltage of the induction motor can be used as an efficient fault indicator to detect and locate interturn short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC fault or unbalanced supply voltage. This paper presents a feedforward multilayer-perceptron neural network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift between the line current and phase voltage to detect ITSC fault and unbalanced supply voltage. The data which are required for training and test NN are generated using simulated model including stator fault with different unbalanced supply voltage and ITSC faults. The simulation results are presented to verify the superior accuracy of proposed method in detecting and discriminating of ITSC fault and unbalanced voltage.