Fault Diagnosis in Transformer Based on Weighted Degree of Grey Slope Incidence of Optimized Entropy

Anping Zhang,Sujie Geng

doi:10.1051/matecconf/20167701004

Abstract

Dissolved gas analysis (DGA) is an important method to find the hidden or incipient insulation faults of oil-immersed power transformer. However, code deficiency exists in the gas ratio methods specified by the IEC standard and complexity of fault diagnosis for power transformer. Hence a new model based on optimized weighted degree of grey slope incidence was put forward. Firstly, the entropy weight is used to determine objective weight of indices; then the model fault types are obtained by weighted degree of grey slope incidence. The combination of entropy weight with grey slope incidence analysis can fully utilize over all information of DGA and give full play to the superiority of grey slope incidence, which overcomes shortcomings of original grey slope incidence analysis. The experimental results also demonstrate that the improved method has higher accuracy compared with three-ratio method and general grey slope incidence analysis method. The diagnosis accuracy is 92.8%.

Highlights

Electric transformer is the most important and most expensive equipment, and one of the most accident-prone equipments in electric systemIn order to ensure safety and credibility of the transformer, it is important to find out the potential fault in the transformer as soon as possible and forecast the development trend of fault
Since the incidence coefficients of general grey slope incidence analysis method fluctuate easilycombining with the analysis of grey sequence entropy, this paper presents the model based on weighted degree of grey slope incidence of optimized entropy [12]
In order to test the efficiency of the method based on calculate the accuracy of fault diagnosis, and the weighted grey slope incidence by entropy compare it with IEC three-ratio method and general grey optimization, we randomly select 300 groups data of slope incidence method

Summary

Introduction

Electric transformer is the most important and most expensive equipment, and one of the most accident-prone equipments in electric systemIn order to ensure safety and credibility of the transformer, it is important to find out the potential fault in the transformer as soon as possible and forecast the development trend of fault. Many scholars both at home and abroad have done a lot of research in the field of transformer fault diagnosis They put forward a lot of methods, such as combining dissolved gas analysis with fuzzy logic, rough set theory, Bayesian networks, artificial neural networks, artificial immune, new radial basis function networks, genetic algorithm and support vector machine. These methods are relatively effective for transformer fault diagnosis, but each has advantages and disadvantages. The classification capability is not satisfactory under the incomplete information [6,7]

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Results

Conclusion