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Evolution of graphical methods for the identification of insulation faults in oil-immersed power transformers: A review

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  • Bustamante, Sergio
  • Manana, Mario
  • Arroyo, Alberto
  • Laso, Alberto
  • Martinez, Raquel

Abstract

The power transformer is one of the most important and critical assets involved in the grid and, at the same time, one of the most expensive. Several transformer condition parameters allow to assess the degradation of assets. These help in the decision-making process on the operation, repair, refurbishment, or replacement of transformers. Dissolved gas analysis (DGA) is one of the most commonly used methods to manage maintenance and establish the health index of power transformers, as well as to identify the type of fault. This paper examines and explores the studies related to graphical methods for the identification of faults in power transformers that had been developed over the last almost 50 years. The main types of faults and the sub-types presented in the analysed studies are compiled in this paper. The main differences between the methods in terms of their graphical representation, number of gases used, type of data used from the DGA results, and number of faults and sub-faults identifiable in each of them, are also presented. The application of the reviewed methods was carried out using two real DGA results.

Suggested Citation

  • Bustamante, Sergio & Manana, Mario & Arroyo, Alberto & Laso, Alberto & Martinez, Raquel, 2024. "Evolution of graphical methods for the identification of insulation faults in oil-immersed power transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:rensus:v:199:y:2024:i:c:s1364032124001965
    DOI: 10.1016/j.rser.2024.114473
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    References listed on IDEAS

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    1. Wani, Shufali Ashraf & Rana, Ankur Singh & Sohail, Shiraz & Rahman, Obaidur & Parveen, Shaheen & Khan, Shakeb A., 2021. "Advances in DGA based condition monitoring of transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    2. de Faria, Haroldo & Costa, João Gabriel Spir & Olivas, Jose Luis Mejia, 2015. "A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 201-209.
    3. Luiz Cheim & Michel Duval & Saad Haider, 2020. "Combined Duval Pentagons: A Simplified Approach," Energies, MDPI, vol. 13(11), pages 1-12, June.
    4. Azmi, A. & Jasni, J. & Azis, N. & Kadir, M.Z.A. Ab., 2017. "Evolution of transformer health index in the form of mathematical equation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 687-700.
    5. Vasiliki Rokani & Stavros D. Kaminaris & Petros Karaisas & Dimitrios Kaminaris, 2023. "Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques," Mathematics, MDPI, vol. 11(22), pages 1-33, November.
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