基于定向马氏距离(DMD)的水冷发电机定子绕组健康诊断

K. Park, B. Youn, J. Yoon, Chao Hu, H. Kim, Y. Bae
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引用次数: 1

摘要

发电机是电厂的关键部件之一,它可能会发生一系列意想不到的系统故障,造成巨大的维护和社会成本。提出了一种新的水冷发电机绕组吸湿健康诊断方法。该诊断方法的主要思想是基于健康数据相关矩阵的定向马氏距离(DMD)。在本研究中,从绕组绝缘体测量的电容数据被称为健康数据。它是通过漏水间接测量发电机绕组吸湿量的一种方法。来自10台发电机(每台发电机有42个绕组)的数据用于演示所建议的发电机绕组健康诊断方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Health diagnostics of water-cooled power generator stator windings using a Directional Mahalanobis Distance (DMD)
A power generator, one of the most critical components in power plant, could experience unexpected series system failures, resulting in substantial maintenance and societal cost. This paper proposes a new health diagnostics method for water-cooled power generator windings against moisture absorption. The main idea of the proposed diagnostics method is a Directional Mahalanobis Distance (DMD) based on the correlation matrix of health data. In this study capacitance data measured from a winding insulator is referred to as health data. It is an indirect measure of moisture absorption in power generator windings by water leakage. Data from ten generators, of which each has 42 windings, are used for demonstration of the proposed health diagnostics method for power generator windings.
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