Transformer Condition Assessment Using Dissolved Gas Analysis

Part of the Power Systems book series (POWSYS)


The dissolved gas analysis (DGA) of transformers can provide an insight view related to thermal and electrical stresses during operations of oil-immersed power transformers. DGA has been practised widely to detect incipient transformer faults and can therefore prevent any further damage to transformers. This chapter focuses on a literature review concerning conventional DGA techniques, as well as the recent advance in DGA diagnostic techniques. Firstly the gas evolution in a transformer is introduced. Various conventional DGA diagnosis methods are then presented, which are usually combined to give a comprehensive view of internal characteristics of transformers, such as the Rogers ratio method, the key gas method, the gassing ratio method, etc. Finally, a brief introduction to the diagnostic techniques using CI for DGA are presented.


Fault Type Partial Discharge Cellulose Insulation Gassing Rate Fault Classification System 
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.


  1. 1.
    Heathcote MJ (1998) The J&P transformer book, 12th edn. First published by Johnson & Phillips Ltd, Newnes imprint, UKGoogle Scholar
  2. 2.
    International Electrotechnical Commission (1978) IEC60559: interpretation of the analysis of gases in transformers and other oil-filled electrical equipment in service. International Electrotechnical Commission Standard, Geneva, SwitzerlandGoogle Scholar
  3. 3.
    The Institute of Electrical and Electronics Engineers (1994) Transformers Committee of the IEEE Power Engineering Society, IEEE guide for the interpretation of gases generated in oil immersed transformers, IEEE Std. C57.104–1991. The Institute of Electrical and Electronics Engineers, Inc., New YorkGoogle Scholar
  4. 4.
    Mollmann A, Pahlavanpour B (1999) New guidelines for interpretation of dissolved gas analysis in oil-filled transformers. Electra CIGRE France 186:30–51Google Scholar
  5. 5.
    Bureau of Standards for the P.R.China (1987) GB7252-87: Guide for the analysis and diagnosis of gases dissolved in transformer oil, National Technical Committee 44 on Transformer of Standardization Administration of ChinaGoogle Scholar
  6. 6.
    Rogers RR (1978) IEEE and IEC codes to interpret incipient faults in transformers using gas in oil analysis. IEEE Trans Electr Insul 13(5):348–354CrossRefGoogle Scholar
  7. 7.
    Liu YL, Griffin PJ, Zhang Y, Ding X (1996) An artificial neural network approach to transformer fault diagnosis. IEEE Trans Power Deliv 11(4):1838–1841Google Scholar
  8. 8.
    Griffin PJ, Wang ZY, Liu YL (1998) A combined ANN and expert system tool for transformer fault diagnosis. IEEE Trans Power Deliv 13(4):1224–1229CrossRefGoogle Scholar
  9. 9.
    Islam SM, Wu T, Ledwich G (2000) A novel fuzzy logic approach to transformer fault diagnosis. IEEE Trans Dielectr Electr Insul 7(2):177–186CrossRefGoogle Scholar
  10. 10.
    Yang HT, Liao CC, Chou JH (2001) Fuzzy learning vector quantization networks for power transformer condition assessment. IEEE Trans Dielectr Electr Insul 8(1):143–149CrossRefGoogle Scholar
  11. 11.
    Lin CE, Ling JM, Huang CL (1993) An expert system for transformer fault diagnosis and maintenance using dissolved gas analysis. IEEE Trans Power Deliv 8(1):231–238CrossRefGoogle Scholar
  12. 12.
    Huang YC, Yang HZ, Huang CL (1997) Developing a new transformer fault diagnosis system through evolutionary fuzzy logic. IEEE Trans Power Deliv 12(2):761–767CrossRefGoogle Scholar
  13. 13.
    Mori E et al (1999) Latest diagnostic methods of gas-in-oil analysis for oil-filled transformer in Japan. In: Proceedings of 13th international conference on dielectric liquids (ICDL’99), Nara, Japan, 20–25 July 1999Google Scholar

Copyright information

© Springer-Verlag London Limited  2011

Authors and Affiliations

  1. 1.Department of Electrical Engineering and ElectronicsThe University of LiverpoolLiverpoolUK

Personalised recommendations