Advances in DGA based condition monitoring of transformers: A review

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

Renewable and Sustainable Energy Reviews, 2021, vol. 149, issue C

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.

Keywords: Transformer-monitoring; Incipient faults; Uncertain diagnosis; Intelligent-techniques; Integrated-methods; Statistical/ mathematical tools; Sensor-based monitoring (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.rser.2021.111347

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