Journal of
Electrical and Electronics Engineering Research

  • Abbreviation: J. Electrical Electron. Eng. Res.
  • Language: English
  • ISSN: 2141-2367
  • DOI: 10.5897/JEEER
  • Start Year: 2009
  • Published Articles: 59

Full Length Research Paper

Artificial neural networks applied to DGA for fault diagnosis in oil-filled power transformers

Mohammad Golkhah1*, Sahar Saffar Shamshirgar2 and Mohammad Ali Vahidi3
1Department of Electrical Engineering University of Manitoba, Winnipeg, Canada. 2Islamic Azad University of Sciences and Researches, Tehran, Iran. 3K. N. Toosi University of Technology, Tehran, Iran.
Email: [email protected]

  •  Accepted: 03 December 2010
  •  Published: 31 January 2011


Dissolved Gas Analysis (DGA) is a popular method to detect and diagnose different types of faults occurring in power transformers. This objective is obtained by employing different interpretations of dissolved gases in the mineral oil insulation of such transformers. This paper engages these interpretations and applies appropriate Artificial Neural Networks (ANN) to classify the different faults. Each interpretation method needs special neural network to determine the occurred fault. Three ANNs are applied to this aim. The classification results and some typical examples are presented to validate the networks.


Key words: DGA, duval triangle, ANN, power transformer faults.