基于神经网络的变压器早期故障检测

Tapsi Nagpal, Y. S. Brar
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引用次数: 5

摘要

电力变压器故障最常用的诊断方法是变压器油溶解气体分析(DGA)。各种解释DGA结果的方法已经发展起来,如关键气体法和罗杰比率法。该方法采用IEC 60599比值法进行变压器故障判别,其优点是采用三种气体比,而不是采用其他比值法中的四种气体比。在某些情况下,DGA结果与现有代码不匹配,导致对多个故障的诊断失败。为了克服这个问题,作者提出了使用神经网络来突出其检测变压器早期故障的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural network based transformer incipient fault detection
The most common diagnosis method for power transformer faults is the dissolved gas analysis (DGA) of transformer oil. Various methods have been developed to interpret DGA results such as key gas method, and roger's ratio method. The present approach utilizes IEC 60599 ratio method to discriminate fault in transformers, which is having the advantage of usage of three gas ratios instead of four gas ratios used in other ratio methods. In some cases, the DGA results cannot be matched by the existing codes, making the diagnosis unsuccessful in multiple faults. To overcome this, the authors have proposed the use of neural networks to highlight their ability to detect the incipient faults in transformer.
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