Transformer Incipient Fault Diagnosis Using Artificial Neural Network

  • Nandkumar Wagh
  • Dinesh Deshpande
Part of the Communications in Computer and Information Science book series (CCIS, volume 250)

Abstract

This paper presents the artificial neural network approach for incipient fault diagnosis of power transformers filled with oil.DGA data from reputed testing unit is obtained to deal with all possible faulty conditions in a power transformer. Well designed artificial neural network having the adaptive features and fast diagnosis capabilities are proposed and testing and training results of DGA samples made available are presented using neural network tool in Matlab 7.10. The diagnosis accuracy obtained during training and testing of samples is better. Programming features are incorporated proposing appropriate preventive maintenance action represented by a type of fault, so that the transformer in service can be saved.

Keywords

Artificial Neural Network Hide Layer Fault Diagnosis Connection Weight Power Transformer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Interpretation of the Analysis of Gases in Transformer and other Oil-filled Electrical Equipment in service, IEC Publication 599 (1978)Google Scholar
  2. 2.
    Rogers, R.R.: IEEE and IEC codes to interpret incipient faults in transformers using gas in oil analysis. IEEE Trans. Electrical Insulation 13(5), 349–354 (1978)CrossRefGoogle Scholar
  3. 3.
    IEEE Guide for the interpretation of Gases Generate In Oil-immersed Transformers, ANSI/IEEE std C57.104 (199, 1992)Google Scholar
  4. 4.
    Zhang, Y., Ding, X., Liu, Y., Griffin, P.J.: An Artificial neural network approach to transformer fault diagnosis. IEEE Trans. Power Delivery 11(4), 1836–1841 (1996)CrossRefGoogle Scholar
  5. 5.
    Xu, W., Wang, D., Zhou, Z., Chen, H.: Fault diagnosis of power transformers: application of fuzzy set theory, expert systems and artificial neural networks. IEE Proceedings-Science Management and Technology 144(1), 39–44 (1997)CrossRefGoogle Scholar
  6. 6.
    Wang, Z., Liu, Y., Griffin, P.J.: A Combine ANN and expert System tool for transformer fault diagnosis. IEEE Trans. Power Delivery 13(4), 1224–1229 (1998)CrossRefGoogle Scholar
  7. 7.
    Huang, Y.-C.: Evolving Neural Nets for Fault Diagnosis of Power Transformer. IEEE Trans.on Power Delivery 18(3), 843–848 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nandkumar Wagh
    • 1
  • Dinesh Deshpande
    • 1
  1. 1.Electrical Engineering DepartmentMaulana Azad National Institute of TechnologyBhopalIndia

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