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Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases

机译:基于五种可燃气体的电力变压器中溶解气体分析的新图形技术的开发

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摘要

Several methods have been proposed for dissolved gas analysis (DGA) of mineral oil in power transformers. One of the simple and most widely used methods is Duval triangle. However, Duval triangle does not consider the concentrations of two combustible gases, namely ethane (C2H6) and hydrogen (H2). As a result, Duval triangle exhibits a lower accuracy in diagnosing certain fault types, for which these gases are the key gases, such as low overheating and corona discharge. Accordingly, this paper proposes a novel graphical technique for DGA based on all the five combustible gases. The proposed graphical technique is developed in the form of a pentagon shape. The pentagon heads represents the percentage concentration of each individual gas to the total combustible gases. The corresponding point for a certain faulty case is determined by the center of mass of all pentagon heads. The knowledge extracted from previous DGA methods and field experiences are used to estimate the preliminary fault regions within the pentagon. The exact boundaries between fault regions are then specified using actual DGA data with corresponding fault types collected from the Egyptian Electricity Network and published cases. The overall performance of the proposed pentagon has been evaluated using a set of fault cases and it is revealed that the proposed pentagon has higher diagnostic accuracy compared to other methods including Duval triangles and IEC standard 60599.
机译:已经提出了几种用于电力变压器中矿物油的溶解气体分析(DGA)的方法。一种简单且使用最广泛的方法是Duval三角形。但是,Duval三角不考虑两种可燃气体(乙烷(C2H6)和氢气(H2))的浓度。结果,Duval三角形在诊断某些故障类型时表现出较低的准确性,这些故障类型的主要气体是这些气体,例如低过热和电晕放电。因此,本文提出了一种基于所有五种可燃气体的DGA的新颖图形技术。所提出的图形技术以五边形的形式发展。五边形头代表每种单独的气体相对于总可燃气体的百分比浓度。某些故障情况下的相应点由所有五边形头部的质心确定。从以前的DGA方法和现场经验中提取的知识用于估计五边形内的初步断层区域。然后,使用实际的DGA数据指定故障区域之间的确切边界,并从埃及电力网络和已发布的案例中收集相应的故障类型。已使用一系列故障案例评估了所建议的五边形的总体性能,并且发现与其他方法(包括Duval三角形和IEC标准60599)相比,所建议的五边形具有更高的诊断精度。

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