Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators

Vahid Behjat, Reza Emadifar, Mehrdad Pourhossein, U. Mohan Rao, Issouf Fofana and Reza Najjar
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Vahid Behjat: Department of Applied Sciences, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Reza Emadifar: Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz 51368, Iran
Mehrdad Pourhossein: Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz 51368, Iran
U. Mohan Rao: Department of Applied Sciences, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Issouf Fofana: Department of Applied Sciences, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Reza Najjar: Faculty of Chemistry, University of Tabriz, Tabriz 51368, Iran

Energies, 2021, vol. 14, issue 13, 1-13

Abstract: Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers.

Keywords: transformer; condition assessment; degradation; furan; methanol; multi-layer perceptron (MLP) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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