International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume5,February 2018,

Topic : DGA Based Incipient Fault Diagnosis of Transformer Using AI Technique

Authors:Ashwini D. Vajirabadkar || Sandip M. Apte

Abstract:In this paper, we focus on a Smart Fault Diagnostic Approach (SFDA) based on the integration of the output results of recognized dissolved gas analysis (DGA) techniques. These techniques are Dornenburg method, Electro-technical Commission standard (IEC) Code, the Central Electricity Generating Board (CEGB) Code based on Rogers’ four ratios, Rogers method given in IEEE-C57 standard, and the Duval triangle. The artificial intelligence model will be constructed to monitor the transformer fault conditions. The fault decision of AI model will supply the proposed integrated SFDA. The integration between these DGA approaches will not only improves the fault condition monitoring of the transformers but also overcomes the individual weakness and the differences between the above methods. Toward a better diagnostic scheme, a new SFDA will be developed based on the integration of the most three appropriate DGA methods .

Keywords: DGA (dissolved gas analysis)

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DOI: 01.1617/vol5/iss2/pid78046


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Editor-in-Chief

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Dr. Allon Guez
Professor, Drexel University,
USA


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IMPACT FACTOR: 4.890

ISSN(Online):2394-2320

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
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