IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p3977-d587387.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/3977/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/13/3977/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Felipe M. Laburú & Thales W. Cabral & Felippe V. Gomes & Eduardo R. de Lima & José C. S. S. Filho & Luís G. P. Meloni, 2024. "New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers," Energies, MDPI, vol. 17(12), pages 1-20, June.
    2. Dimitris A. Barkas & Stavros D. Kaminaris & Konstantinos K. Kalkanis & George Ch. Ioannidis & Constantinos S. Psomopoulos, 2022. "Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning," Energies, MDPI, vol. 16(1), pages 1-17, December.
    3. Firas B. Ismail & Maisarah Mazwan & Hussein Al-Faiz & Marayati Marsadek & Hasril Hasini & Ammar Al-Bazi & Young Zaidey Yang Ghazali, 2022. "An Offline and Online Approach to the OLTC Condition Monitoring: A Review," Energies, MDPI, vol. 15(17), pages 1-18, September.
    4. Ancuța-Mihaela Aciu & Sorin Enache & Maria-Cristina Nițu, 2024. "A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis," Energies, MDPI, vol. 17(10), pages 1-26, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3977-:d:587387. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.