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STATISTICAL HEALTH DIAGNOSTICS FOR WATER-COOLED POWER GENERATOR STATOR WINDING AGAINST WATER ABSORPTION

机译:水冷式发电机定子绕组吸水的统计健康诊断

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One of most important components in power generator is a stator winding since an unexpected failure of the water absorbed-winding leads to plant shut-down and substantial loss. Typically the stator winding is maintained with a time- or usage-based strategy, which could result in substantial waste of remaining life, high maintenance cost and low plant availability. Recently, the field of prognostics and health management offers general diagnostic and prognostic techniques to precisely assess the health condition and robustly predict the remaining useful life of an engineered system, with an aim to address the aforementioned deficiencies. This research aims at developing health reasoning system of power generator stator winding with physical and statistical analysis against water absorption. And it is based upon the capacitance measurements on winding insulations. In particular, a new health measure, Directional Mahalanobis Distance (DMD), is proposed to quantify the health condition. In addition, the empirical health grade system based upon the proposed technique, DMD, is carried out with the maintenance history. The smart health reasoning system is validated using eight years' field data from eight generators, each of which contains forty two windings.
机译:发电机中最重要的部件之一是定子绕组,因为吸水绕组的意外失效导致植物关闭和大量损失。通常,定子绕组由基于时间或使用的策略保持,这可能导致剩余寿命,高维护成本和低植物可用性的大量浪费。最近,预测和健康管理领域提供了一般诊断和预后技术,以精确评估健康状况,并强大地预测工程系统的剩余使用寿命,目的是解决上述缺陷。本研究旨在开发发电机定子绕组的健康推理系统,用物理和统计分析抵抗吸水性。并且它基于绕组绝缘的电容测量。特别是,建议建议新的健康措施,定向马哈拉诺比斯距离(DMD)量化健康状况。此外,基于所提出的技术DMD的经验健康级系统与维护历史进行。智能健康推理系统使用来自八个发电机的八年的现场数据进行验证,每个字段数据包含四十个两个绕组。

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