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Obtaining a high Accurate Fault Classification of Power Transformer based on Dissolved Gas Analysis using ANFIS

Author Affiliations

  • 1 PG Department of Electronics Engg., Bapurao Deshmukh College of Engg, Sewagram, MS, INDIA

Res. J. Recent Sci., Volume 1, Issue (2), Pages 97- 99, February,2 (2012)


Power Transformers are a vital link in a power system. Well-being of power transformer is very much important to the reliable operation of the power system. Dissolved Gas Analysis (DGA) is one for the effective tool for monitoring the condition of the transformer. To interpret the DGA result multiple techniques are available.IEC codes are developed to diagnose transformer faults. But there are cases of errors and misleading judgment due to borderline and multiple faults. Methods were developed to solve this problem by using fuzzy membership functions to map the IEC codes and heuristic experience to adjust the fuzzy rule. This paper proposes a neuro-fuzzy method to perform self learning and auto rule adjustment for producing best rules.


  1. Singh A. and Verma P., A review of intelligent diagnostic methods for condition assessment of insulation system in power transformers in Condition Monitoring and Diagnosis, 2008 CMD, international Conference, 1354-1357 (2008)
  2. Kelly J.J., Transformer Fault Diagnosis by dissolved gas analysis, IEEE Trans on Industry Applications, 16(4), 777-782 (1980)
  3. Hongzhong M., Zheng L. and Ju. H. Jingdong P., Diagnosis of Power Transformer Faults on Fuzzy Three Ratio Method in Power Engineering Conference, 1-456 (2005)
  4. Rogers R., IEEE and IEC Codes to interpret incipient daults in transformer, using gas in oil analysis, IEEE, Trans. on Electr. Insu.13(5), 349-354 (1978)
  5. Su Q., Lai L.L. and Austin P., A fuzzy dissolved gas analysis method for the diagnosis of diagnosis of multiple incipient fault in a transformer, IEEE Transactions on Power System, 593-598 (2000)
  6. Zhenyuan W., Yilu L. and Griffin P.J., Neural net and expert systems diagnose transformer faults, Computer Applications in Power IEEE 13, 50-55 (2000)
  7. Mofizul S., Islam T. Wu and Ledwich G., A novel fuzzy logic approach to transformer fault diagnosis Dielectric and Electrical insulation IEEE transaction on 177-186 (2000)
  8. Wang Z.Y., Liu Y.L. and Griffin P.J, A combined ANN and expert system tool for transformer fault diagnosis. Power Engineering Society Winter meeting, IEEE 12, 23-27 (2000)
  9. Duval M., New Techniques for dissolved gas in oil analysis, Electrical insulation magazine, IEEE (19) 6-15, (2003)
  10. Muhamad N.A., Phung B.T. and Blackburn T.R., Comparative study and analysis of DGA methods for mineral oil using fuzzy logic, in power engineering conference 1301-1306 (2007)