Lashkari Negin, Poshtan Javad, Azgomi Hamid Fekri
Iran University of Science and Technology, Iran.
ISA Trans. 2015 Nov;59:334-42. doi: 10.1016/j.isatra.2015.08.001. Epub 2015 Sep 26.
The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator 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 or supply voltage fault. 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. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method.
感应电动机的线电流与相电压之间的三相位移可作为一种有效的故障指示器，用于检测和定位定子匝间短路（ITSC）故障。然而，供电电压不平衡是不可避免地影响定子电流进而影响三相位移的因素之一。因此，有必要提出一种能够识别三相电流不平衡是由ITSC还是供电电压故障引起的方法。本文提出了一种基于监测负序电压和三相位移的、通过反向传播训练的前馈多层感知器神经网络（NN）。训练和测试NN所需的数据是使用定子仿真模型生成的。给出了实验结果以验证所提方法的卓越准确性。
