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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 |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 21 |
| Year of Publication: 2012 |
| Authors: Hasmat Malik, Tarkeshwar, Mantosh Kr, Amit Kr Yadav, B.anil Kr |
10.5120/5842-8057
|
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
Abstract
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.
References
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