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Park's Vector Approach to detect an inter turn stator fault in a doubly fed induction machine by a neural network

机译:派克矢量法通过神经网络检测双馈感应电机匝间定子故障

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An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach , using a neural network s.
机译:在初始阶段未发现的电机故障可能会成为灾难性事故,并且可能遭受严重破坏。因此,未检测到的机器故障可能会导致故障级联,进而可能导致生产停工。就生产时间的损失,维护成本和原材料的浪费而言,这种停机的成本很高。双馈感应发电机主要用于兆瓦级发电厂的风能转换。本文提出了一种双馈感应电机中匝间定子故障的检测方法,该电机的定子和转子由两个脉宽调制(PWM)逆变器供电。本文中用于检测此故障的方法是基于使用神经网络s的Park矢量方法。

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