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No-codes scoring diagnostic method for DGA of power transformer

机译:电力变压器DGA的无代码评分诊断方法

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摘要

The dissolved gas analysis (DGA) technique is widely accepted in identification of incipient faults for power transformer. However, the diagnostic accuracy of classical DGA methods is compromised by absolute code boundary or noise-corrupted data. To resolve these problems, this paper presents a novel scoring method based on strong correlation gas ratios, weights, and effective interval under medium-sized data samples. By means of the ratios selected by coefficient of variation and calculation of weights by technique for order preference by similarity to an ideal solution, a score formula can be built. Taking the ratios as the input of formula, the score can be calculated and the specific fault can be identified by looking-up score-to-fault table. This method can substantially circumvent those problems of conventional DGA methods by score complementarity, which functions as the score calculated by low-quality data can offset by a high-quality one, and a comprehensive analysis can be done.
机译:溶解气体分析(DGA)技术在识别电力变压器的初始故障中被广泛接受。但是,经典DGA方法的诊断准确性被绝对代码边界或噪声损坏的数据损害。为了解决这些问题,本文提出了一种基于中等数据样本强的相关气体比,重量和有效间隔的新型评分方法。借助于通过通过与理想解决方案相似性的顺序偏好的技术通过改变系数和权重的计算来选择的比率,可以构建分数公式。将比例作为公式的输入,可以计算得分,并且可以通过查找分数到故障表来识别特定故障。该方法可以通过得分互补性基本上绕过传统DGA方法的那些问题,其用作通过低质量数据计算的分数可以通过高质量的数据来抵消,并且可以进行全面的分析。

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