NDLI: Study and diagnosis of key gases to assess condition of power transformers

Content Provider	IEEE Xplore Digital Library
Author	Merita, A.K. Sharma, R.N. Chauhan, S.
Copyright Year	2011
Description	Author affiliation: EED, NIT Hamirpur, H.P. India (Merita, A.K.; Sharma, R.N.; Chauhan, S.)
Abstract	The operating principle of transformers is based on the slight albeit harmless deterioration of the insulation that accompanies incipient faults, in the form of arcs or peaks resulting from dielectric breakdown of weak or overstressed parts of the insulation, or hot spots due to abnormally high current densities in conductors. Whatever the cause, these stresses will result in the chemical breakdown of some of the oil or cellulose molecules constituting the dielectric insulation. The main degradation products are gases, which entirely or partially dissolve in the oil under thermal and electrical stress conditions caused by faults currents in the transformers, the hydrocarbon molecules of mineral oil can decompose and form active hydrogen and hydrocarbon fragments and these fragments can combine with each other to form gases like hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6) etc. The major contribution of present work is the study of different methods of Dissolved Gas Analysis for the incipient fault diagnosis of transformer also gives diagnosis to the variation of key gases of 110 different transformers of state electricity board to analyse condition of power transformer irrespective of manufacturing year and make.
Starting Page	343
Ending Page	347
File Size	320161
Page Count	5
File Format	PDF
ISBN	9781424486786
e-ISBN	9781424486793
DOI	10.1109/ICECTECH.2011.5941917
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-08
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gases Artificial neural networks Oil insulation Dissolved gas analysis Corona Power transformers Power transformer Artificial intelligence Key gases
Content Type	Text
Resource Type	Article

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