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

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  • Vahid Behjat

    (Department of Applied Sciences, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
    Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz 51368, Iran)

  • 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)

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.

Suggested Citation

  • Vahid Behjat & Reza Emadifar & Mehrdad Pourhossein & U. Mohan Rao & Issouf Fofana & Reza Najjar, 2021. "Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators," Energies, MDPI, vol. 14(13), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3977-:d:587387
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    References listed on IDEAS

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    1. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
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    Cited by:

    1. Andrew Adewunmi Adekunle & Samson Okikiola Oparanti & Issouf Fofana, 2023. "Performance Assessment of Cellulose Paper Impregnated in Nanofluid for Power Transformer Insulation Application: A Review," Energies, MDPI, vol. 16(4), pages 1-32, February.
    2. Bonginkosi A. Thango, 2022. "Feedforward Artificial Neural Network (FFANN) Application in Solid Insulation Evaluation Methods for the Prediction of Loss of Life in Oil-Submerged Transformers," Energies, MDPI, vol. 15(22), pages 1-11, November.
    3. Enze Zhang & Jiang Liu & Chaohai Zhang & Peijun Zheng & Yosuke Nakanishi & Thomas Wu, 2023. "State-of-Art Review on Chemical Indicators for Monitoring the Aging Status of Oil-Immersed Transformer Paper Insulation," Energies, MDPI, vol. 16(3), pages 1-31, January.
    4. Alexander S. Karandaev & Igor M. Yachikov & Andrey A. Radionov & Ivan V. Liubimov & Nikolay N. Druzhinin & Ekaterina A. Khramshina, 2022. "Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition," Energies, MDPI, vol. 15(10), pages 1-21, May.

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