DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations


  • Jyoti Singh
  • Dr. Prateek Nigam
  • Achie Malviya






Power transformers are essential devices for the durable and reliable performance of an electrical system. the main objective of this study is to analyze three classical diagnosis techniques to identify incipient faults in Transformer oil using Rogers’s Ratio Method, Doernenburg Ratio Method, and ANN which is a type of artificial intelligence learning method. Implementation of the system in MATLAB software for each diagnosis method and compare their accuracy and efficiency and hence design three diagnosis methods of DGA for condition assessment of Power Transformer. And the analysis on the MATLAB software shall be carried so as to detect the best method for detection of a certain type of fault and the best suited method for overall fault analysis for a certain data sets out of the three methods. This technique utilizes the learning capacity of that artificial neural network has been shown to be more efficient in detecting different mistakes. The overall error detection accuracy of such gas neural network study was found to be 73.8 percent.


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Author Biographies

Jyoti Singh

M Tech Scholar

Rabindranath Tagore University

Bhopal, M.P, India

Dr. Prateek Nigam


Rabindranath Tagore University

 Bhopal, M.P, India

Achie Malviya

Assistant Professor

Rabindranath Tagore University

Bhopal M. P, India


S. S. M. Ghoneim and I. B. M. Taha, “A new approach of DGA interpretation technique for transformer fault diagnosis,” International Journal of Electrical Power and Energy Systems, vol. 81, pp. 265–274, 2016.

T. Kari, W. Gao, D. Zhao et al., “An integrated method of ANFIS and Dempster-Shafer theory for fault diagnosis of power transformer,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 25, no. 1, pp. 360–371, 2018.

M. Arshad, S. Islam, and A. Khaliq, “Fuzzy logic approach in power transformers management and decision making,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 21, no. 5, pp. 2343–2354, 2014.

O. E. Gouda, S. M. Saleh, and S. H. El-hoshy, “Power transformer incipient faults diagnosis based on dissolved gas analysis,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 1, no. 1, pp. 10–16, 2016.

G. K. Irungu, A. O. Akumu, and J. L. Munda, “Application of fuzzy logic and evidential reasoning methodologies in transformer insulation stress assessment,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, no. 3, pp. 1444–1452, 2016.

N. A. Bakar and A. Abu-Siada, “Fuzzy logic approach for transformer remnant life prediction and asset management decision,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, no. 5, pp. 3199–3208, 2016.

Edwell T. Mharakurwa, G. N. Nyakoe “Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique” Journal of Electrical and Computer Engineering / 2019.

N.C. Joshi, Y.R. Sood “Transformer Internal Winding Faults Diagnosis Methods: A Review” MIT International Journal of Electrical and Instrumentation Engineering, Vol. 2, No. 2, Aug. 2012, pp. (77-81).

Yan Wang, Liguo Zhang “A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment” Mathematical Problems in Engineering / 2017

Xue Wang, Tao Han “Transformer Fault Diagnosis Based on Stacking Ensemble Learning” https://doi.org/10.1002/tee.23247, 02 October 2020.




How to Cite

Singh, J. ., Nigam, D. P. ., & Malviya, A. (2021). DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations. SMART MOVES JOURNAL IJOSCIENCE, 7(10), 1–9. https://doi.org/10.24113/ijoscience.v7i10.413