IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4448-d1160601.html
   My bibliography  Save this article

Planning and Optimizing the Replacement Strategies of Power Transformers: Literature Review

Author

Listed:
  • Jefferson Zuñiga Balanta

    (Electrical Engineering Institute (IEE), National University of San Juan (UNSJ), CONICET, Av. Libertador Gral. San Martín 1109, San Juan J5400, Argentina)

  • Sergio Rivera

    (EMC-UN-Electromagnetic Compatibility Research Group, Universidad Nacional de Colombia, Cra 45, Bogotá 111321, Colombia)

  • Andrés A. Romero

    (Electrical Engineering Institute (IEE), National University of San Juan (UNSJ), CONICET, Av. Libertador Gral. San Martín 1109, San Juan J5400, Argentina)

  • Gustavo Coria

    (Electrical Engineering Institute (IEE), National University of San Juan (UNSJ), CONICET, Av. Libertador Gral. San Martín 1109, San Juan J5400, Argentina)

Abstract

The power transformer is one of the most critical assets in power systems; therefore, planning and optimizing the economic investment for its replacement is crucial for the financial efficiency of the utility. A compilation of the main approaches reported in the literature for the replacement of oil-immersed power transformers is presented in this article. A chronological description of procedures presented in the literature for the determination of risk index, useful life evaluation, and transformer replacements is provided. Methodologies that use the theoretical basis of the degree of polymerization of the solid insulation of the units through the oxidation aging process to estimate their condition bring together the best tools currently available to achieve this objective. However, it is important and pertinent to complement these methodologies by considering the aging processes by pyrolysis and hydrolysis together and by incorporating economic analyses for appropriate replacement and management of these aged units.

Suggested Citation

  • Jefferson Zuñiga Balanta & Sergio Rivera & Andrés A. Romero & Gustavo Coria, 2023. "Planning and Optimizing the Replacement Strategies of Power Transformers: Literature Review," Energies, MDPI, vol. 16(11), pages 1-16, May.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4448-:d:1160601
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4448/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/11/4448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Srdjan Milosavljevic & Aleksandar Janjic, 2020. "Integrated Transformer Health Estimation Methodology Based on Markov Chains and Evidential Reasoning," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, May.
    2. Lefeng Cheng & Tao Yu & Guoping Wang & Bo Yang & Lv Zhou, 2018. "Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study," Energies, MDPI, vol. 11(1), pages 1-26, January.
    Full references (including those not matched with items on IDEAS)

    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. Enwen Li & Linong Wang & Bin Song & Siliang Jian, 2018. "Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data," Energies, MDPI, vol. 11(9), pages 1-17, September.
    2. Przemyslaw Goscinski & Zbigniew Nadolny & Andrzej Tomczewski & Ryszard Nawrowski & Tomasz Boczar, 2023. "The Influence of Heat Transfer Coefficient α of Insulating Liquids on Power Transformer Cooling Systems," Energies, MDPI, vol. 16(6), pages 1-15, March.
    3. Haonan Tian & Zhongbao Wei & Sriram Vaisambhayana & Madasamy Thevar & Anshuman Tripathi & Philip Kjær, 2019. "A Coupled, Semi-Numerical Model for Thermal Analysis of Medium Frequency Transformer," Energies, MDPI, vol. 12(2), pages 1-16, January.
    4. Jonathan Velasco Costa & Diogo F. F. da Silva & Paulo J. Costa Branco, 2022. "Large-Power Transformers: Time Now for Addressing Their Monitoring and Failure Investigation Techniques," Energies, MDPI, vol. 15(13), pages 1-59, June.
    5. 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.
    6. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.

    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:16:y:2023:i:11:p:4448-:d:1160601. 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.