DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

Authors

  • Jyoti Singh
  • Dr. Prateek Nigam
  • Achie Malviya

DOI:

https://doi.org/10.24113/ijoscience.v7i10.413

Keywords:

DGA, ANN, KDD, DST.

Abstract

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

HOD

Rabindranath Tagore University

 Bhopal, M.P, India

Achie Malviya

Assistant Professor

Rabindranath Tagore University

Bhopal M. P, India

References

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Published

10/27/2021

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

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Articles