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Advances in DGA based condition monitoring of transformers: A review

Author

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  • Wani, Shufali Ashraf
  • Rana, Ankur Singh
  • Sohail, Shiraz
  • Rahman, Obaidur
  • Parveen, Shaheen
  • Khan, Shakeb A.

Abstract

Dissolved Gas Analysis (DGA) is a standout diagnostic strategy to recognise incipient faults and monitor the condition of oil-immersed transformers. It correlates the concentration of various insulation degradation by-products dissolved in oil with the nature of faults. DGA standards provide various interpretation methods for fault diagnosis and estimation of the useful life of insulation. These economic and widely used methods have significant limitations that hamper their resourcefulness. The uncertainty in diagnostic outcomes due to manual handling of fault data, boundary conditions, unresolved fault cases due to over-range ratios, inability to diagnose concurrently existing faults (multiple faults) and lack of severity information about incipient faults are worth mentioning. This paper reviews various solutions provided by researchers for addressing these uncertain and unresolved isssues in incipient fault diagnosis. Paper presents a systematic review of the literature that includes the application of intelligent and mathematical techniques in DGA based diagnosis. Further, it critically analyses the reported works on composite methods and finally, progress in sensor-based condition monitoring of transformer is also deliberated. This article is first of its kind where AI (Artificial Intelligence), integrated methods, mathematical and experimental approaches in DGA based diagnostics are simultaneously reviewed and analysed. The paper concludes the best possible solution for reliabile diagnosis and also explores pertinent issues of research in the area of DGA based transformer health monitoring.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:149:y:2021:i:c:s136403212100633x
    DOI: 10.1016/j.rser.2021.111347
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Andrey A. Radionov & Ivan V. Liubimov & Igor M. Yachikov & Ildar R. Abdulveleev & Ekaterina A. Khramshina & Alexander S. Karandaev, 2023. "Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data," Energies, MDPI, vol. 16(12), pages 1-27, June.
    2. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    3. Yang, Zaoli & Shang, Wen-Long & Zhang, Haoran & Garg, Harish & Han, Chunjia, 2022. "Assessing the green distribution transformer manufacturing process using a cloud-based q-rung orthopair fuzzy multi-criteria framework," Applied Energy, Elsevier, vol. 311(C).
    4. Jelke Wibbeke & Payam Teimourzadeh Baboli & Sebastian Rohjans, 2022. "Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach," Energies, MDPI, vol. 15(9), pages 1-13, April.

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