International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 41 - Issue 21 |
Published: March 2012 |
Authors: Hasmat Malik, Tarkeshwar, Mantosh Kr, Amit Kr Yadav, B.Anil Kr |
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Hasmat Malik, Tarkeshwar, Mantosh Kr, Amit Kr Yadav, B.Anil Kr . Application of Physical-Chemical Data in Estimation of Dissolved Gases in Insulating Mineral Oil for Power Transformer Incipient Fault Diagnosis with ANN. International Journal of Computer Applications. 41, 21 (March 2012), 43-50. DOI=10.5120/5842-8057
@article{ 10.5120/5842-8057, author = { Hasmat Malik,Tarkeshwar,Mantosh Kr,Amit Kr Yadav,B.Anil Kr }, title = { Application of Physical-Chemical Data in Estimation of Dissolved Gases in Insulating Mineral Oil for Power Transformer Incipient Fault Diagnosis with ANN }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 41 }, number = { 21 }, pages = { 43-50 }, doi = { 10.5120/5842-8057 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A Hasmat Malik %A Tarkeshwar %A Mantosh Kr %A Amit Kr Yadav %A B.Anil Kr %T Application of Physical-Chemical Data in Estimation of Dissolved Gases in Insulating Mineral Oil for Power Transformer Incipient Fault Diagnosis with ANN%T %J International Journal of Computer Applications %V 41 %N 21 %P 43-50 %R 10.5120/5842-8057 %I Foundation of Computer Science (FCS), NY, USA
In this paper, Artificial Neural Networks are used to solve a complex problem concerning to power transformers and characterized by non-linearity and hard dynamic modeling. The operation conditions and integrity of a power transformer can be detected by analysis of physical-chemical and chromatographic isolating oil, allowing establish procedures for operating and maintaining the equipment. However, while the costs of physical-chemical tests are smaller, the chromatographic analysis is more informative. This work presents an estimation study of the information that would be obtained in the chromatographic test from the physical-chemical analysis through Artificial Neural Networks. Thus, the power utilities can achieve greater reliability in the prediction of incipient failures at a lower cost. The results show this strategy to be a promising, with accuracy of 100% in best cases. The authors have estimated the dissolved gases in insulating mineral oil using proposed method for 185 transformers. As a result, appropriate maintenance scenario can be planned.