IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4697-d848609.html
   My bibliography  Save this article

Large-Power Transformers: Time Now for Addressing Their Monitoring and Failure Investigation Techniques

Author

Listed:
  • Jonathan Velasco Costa

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049 Lisbon, Portugal
    These authors contributed equally to this work.)

  • Diogo F. F. da Silva

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049 Lisbon, Portugal
    These authors contributed equally to this work.)

  • Paulo J. Costa Branco

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049 Lisbon, Portugal)

Abstract

Several review studies exist in the literature about monitoring, fault detection, and diagnosis of power transformers. However, they are general approaches in terms of power transformers. Some only focus on applying a specific class of techniques, but again, for general power transformers. Other reviews focus on applying different technologies such as fiber optics, thermal cameras, and vibration sensors, but all within the perspective of general power transformers. A significant question remains: among all types of power transformers, which specific techniques should be used, and why are they more adequate? What are the uncertainties that can decrease their precision? What about the balance, in terms of costs, associated with applying a certain technique and the return needed for a particular type of transformer? In this context, this paper is not only a literature review of well-known problems related to power transformers. Here, we do not just center on large power transformers (100 MVA or higher). Still, we describe a case study of a phase-shifting 1400 MVA-400 kV three-phase transformer that currently connects two European countries that began to show signs of abnormal operating conditions in 2012. In this way, the need to detect and identify anomalies in their initial stage of development for a possible preventive maintenance action is more than justified, which is essentially achieved with continuous monitoring models of the transformer, as concluded in this paper.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4697-:d:848609
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4697/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/13/4697/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Samuel Brodeur & Van Ngan Lê & Henri Champliaud, 2021. "A Nonlinear Finite-Element Analysis Tool to Prevent Rupture of Power Transformer Tank," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    2. Krzysztof Walczak & Jaroslaw Gielniak, 2021. "Temperature Distribution in the Insulation System of Condenser-Type HV Bushing—Its Effect on Dielectric Response in the Frequency Domain," Energies, MDPI, vol. 14(13), pages 1-18, July.
    3. Xiang Gao & C. Adam Schlosser & Eric R. Morgan, 2018. "Potential impacts of climate warming and increased summer heat stress on the electric grid: a case study for a large power transformer (LPT) in the Northeast United States," Climatic Change, Springer, vol. 147(1), pages 107-118, March.
    4. Patryk Bohatyrewicz & Andrzej Mrozik, 2021. "The Analysis of Power Transformer Population Working in Different Operating Conditions with the Use of Health Index," Energies, MDPI, vol. 14(16), pages 1-14, August.
    5. 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.
    6. 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.
    7. Song Wang & Shuang Wang & Ying Cui & Jie Long & Fuqiang Ren & Shengchang Ji & Shuhong Wang, 2020. "An Experimental Study of the Sweep Frequency Impedance Method on the Winding Deformation of an Onsite Power Transformer," Energies, MDPI, vol. 13(14), pages 1-13, July.
    8. James Dukarm & Zachary Draper & Tomasz Piotrowski, 2020. "Diagnostic Simplexes for Dissolved-Gas Analysis," Energies, MDPI, vol. 13(23), pages 1-16, December.
    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. Alexander I. Balitskii & Vitaly V. Dmytryk & Lyubomir M. Ivaskevich & Olexiy A. Balitskii & Alyona V. Glushko & Lev B. Medovar & Karol F. Abramek & Ganna P. Stovpchenko & Jacek J. Eliasz & Marcin A. K, 2022. "Improvement of the Mechanical Characteristics, Hydrogen Crack Resistance and Durability of Turbine Rotor Steels Welded Joints," Energies, MDPI, vol. 15(16), pages 1-23, August.

    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. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    3. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    4. Patryk Bohatyrewicz & Szymon Banaszak, 2022. "Assessment Criteria of Changes in Health Index Values over Time—A Transformer Population Study," Energies, MDPI, vol. 15(16), pages 1-15, August.
    5. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    6. Georgi Ivanov & Anelia Spasova & Valentin Mateev & Iliana Marinova, 2023. "Applied Complex Diagnostics and Monitoring of Special Power Transformers," Energies, MDPI, vol. 16(5), pages 1-24, February.
    7. 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.
    8. 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.
    9. 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.
    10. Piotr Przybylek, 2018. "A New Concept of Applying Methanol to Dry Cellulose Insulation at the Stage of Manufacturing a Transformer," Energies, MDPI, vol. 11(7), pages 1-13, June.
    11. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.
    12. Andrey A. Radionov & Ivan V. Liubimov & Igor M. Yachikov & Ildar R. Abdulveleev & Ekaterina A. Khramshina & Alexander S. Karandaev, 2023. "Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data," Energies, MDPI, vol. 16(12), pages 1-27, June.
    13. Fabio Henrique Pereira & Francisco Elânio Bezerra & Shigueru Junior & Josemir Santos & Ivan Chabu & Gilberto Francisco Martha de Souza & Fábio Micerino & Silvio Ikuyo Nabeta, 2018. "Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations," Energies, MDPI, vol. 11(7), pages 1-12, June.
    14. 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.
    15. Inmaculada Fernández, 2022. "The Need for Experimental and Numerical Analyses of Thermal Ageing in Power Transformers," Energies, MDPI, vol. 15(17), pages 1-4, September.
    16. Min-Soo Kim & Do-Hyun Kim & Dong-Keun Jeong & Jang-Mok Kim & Hee-Je Kim, 2020. "Soft Start-Up Control Strategy for Dual Active Bridge Converter with a Supercapacitor," Energies, MDPI, vol. 13(16), pages 1-19, August.
    17. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    18. Kakou D. Kouassi & Issouf Fofana & Ladji Cissé & Yazid Hadjadj & Kouba M. Lucia Yapi & K. Ambroise Diby, 2018. "Impact of Low Molecular Weight Acids on Oil Impregnated Paper Insulation Degradation," Energies, MDPI, vol. 11(6), pages 1-13, June.
    19. Qunli Wu & Hongjie Zhang, 2019. "A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    20. 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.

    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:15:y:2022:i:13:p:4697-:d:848609. 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.