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首页> 外文会议>2013 IEEE 1st International Conference on Condition Assessment Techniques in Electrical Systems >Wavelet and SFAM based classification of induction motor stator winding short circuit faults and incipient insulation failures
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Wavelet and SFAM based classification of induction motor stator winding short circuit faults and incipient insulation failures

机译：基于小波和SFAM的感应电动机定子绕组短路故障和初始绝缘故障分类

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摘要

Inter-turn fault in stator windings is one of the prime reasons behind a significant percentage of induction motor failures. Inter-turn short circuit fault may develop either due to complete failure of insulation between turns causing direct interturn short circuit fault or due to partial degradation of insulation between turns resulting in incipient insulation fault. Common protection schemes accompanied with induction motors in industrial applications normally fail to detect these faults especially when a minor number of turns are involved in such faults. Detection of these faults at their early stage can substantially reduce the possibilities of serious damage to the motor and consequently financial losses, environmental damage and probable personnel injury etc. Detection of such faults becomes a tricky task when these two different types of faults exhibit identical unbalance in motor supply currents. In the present study, two different series of experiments were carried out on an induction motor which was subjected to operate under direct short circuit fault and also under equivalent incipient insulation fault in stator winding. Using Extended Park's Vector Approach, Park's Vector Modulus (PVM) have been estimated from captured three phase current signals under different operating conditions of the motor. Several fault features were extracted from AC components present in PVMs by applying Continuous Wavelet Transform (CWT). Then, the fault features were fed to a Simplified Fuzzy Art-Map (SFAM) based classifier which has been found to perform accurately in identifying separately the direct inter-turn short circuit fault levels and equivalent incipient insulation failure conditions.
机译：定子绕组中的匝间故障是导致感应电动机故障百分比显着的主要原因之一。匝间短路故障可能是由于匝间绝缘完全失效导致直接匝间短路故障而引起，也可能是由于匝间绝缘部分退化而导致初期绝缘故障所致。工业应用中与感应电动机一起使用的常见保护方案通常无法检测到这些故障，尤其是当此类故障涉及少量匝数时。在早期发现这些故障可以大大减少严重损坏电动机的可能性，从而减少经济损失，环境破坏和可能的人员伤害等。当这两种不同类型的故障表现出相同的不平衡时，检测此类故障就成为一项棘手的任务。电机电源电流。在本研究中，对感应电动机进行了两个不同系列的实验，该感应电动机在直接短路故障下以及在定子绕组中的等效初始绝缘故障下也进行操作。使用扩展Park的矢量方法，已经从捕获的三相电流信号中估计了Park的矢量模量（PVM），该信号在电动机的不同运行条件下。通过应用连续小波变换（CWT），从PVM中存在的AC组件中提取了一些故障特征。然后，将故障特征输入到基于简化模糊艺术图（SFAM）的分类器中，该分类器在分别识别直接匝间短路故障水平和等效初始绝缘故障条件方面可准确执行。

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