Kolla S, Varatharasa L
Department of Technology Systems, Bowling Green State University, OH 43403, USA.
ISA Trans. 2000;39(4):433-9. doi: 10.1016/s0019-0578(00)00031-8.
This paper presents an artificial neural network (ANN) based technique to identify faults in a three-phase induction motor. The main types of faults considered are overload, single phasing, unbalanced supply voltage, locked rotor, ground fault, over-voltage and under-voltage. Three-phase currents and voltages from the induction motor are used in the proposed approach. A feedforward layered neural network structure is used. The network is trained using the backpropagation algorithm. The trained network is tested with simulated fault current and voltage data. Fault detection is attempted in the no fault to fault transition period. Off-line testing results on a 3 HP induction motor model show that the proposed ANN based method is effective in identifying various types of faults.
本文提出了一种基于人工神经网络（ANN）的技术，用于识别三相感应电动机中的故障。所考虑的主要故障类型包括过载、单相、电源电压不平衡、堵转、接地故障、过电压和欠电压。该方法使用感应电动机的三相电流和电压。采用前馈分层神经网络结构。使用反向传播算法对网络进行训练。使用模拟的故障电流和电压数据对训练后的网络进行测试。尝试在无故障到故障的过渡期间进行故障检测。对一个3马力感应电动机模型的离线测试结果表明，所提出的基于人工神经网络的方法在识别各种类型的故障方面是有效的。
