IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v199y2024ics1364032124001965.html
   My bibliography  Save this article

Evolution of graphical methods for the identification of insulation faults in oil-immersed power transformers: A review

Author

Listed:
  • Bustamante, Sergio
  • Manana, Mario
  • Arroyo, Alberto
  • Laso, Alberto
  • Martinez, Raquel

Abstract

The power transformer is one of the most important and critical assets involved in the grid and, at the same time, one of the most expensive. Several transformer condition parameters allow to assess the degradation of assets. These help in the decision-making process on the operation, repair, refurbishment, or replacement of transformers. Dissolved gas analysis (DGA) is one of the most commonly used methods to manage maintenance and establish the health index of power transformers, as well as to identify the type of fault. This paper examines and explores the studies related to graphical methods for the identification of faults in power transformers that had been developed over the last almost 50 years. The main types of faults and the sub-types presented in the analysed studies are compiled in this paper. The main differences between the methods in terms of their graphical representation, number of gases used, type of data used from the DGA results, and number of faults and sub-faults identifiable in each of them, are also presented. The application of the reviewed methods was carried out using two real DGA results.

Suggested Citation

  • Bustamante, Sergio & Manana, Mario & Arroyo, Alberto & Laso, Alberto & Martinez, Raquel, 2024. "Evolution of graphical methods for the identification of insulation faults in oil-immersed power transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:rensus:v:199:y:2024:i:c:s1364032124001965
    DOI: 10.1016/j.rser.2024.114473
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032124001965
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2024.114473?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Luiz Cheim & Michel Duval & Saad Haider, 2020. "Combined Duval Pentagons: A Simplified Approach," Energies, MDPI, vol. 13(11), pages 1-12, June.
    2. Azmi, A. & Jasni, J. & Azis, N. & Kadir, M.Z.A. Ab., 2017. "Evolution of transformer health index in the form of mathematical equation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 687-700.
    3. Wani, Shufali Ashraf & Rana, Ankur Singh & Sohail, Shiraz & Rahman, Obaidur & Parveen, Shaheen & Khan, Shakeb A., 2021. "Advances in DGA based condition monitoring of transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Vasiliki Rokani & Stavros D. Kaminaris & Petros Karaisas & Dimitrios Kaminaris, 2023. "Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques," Mathematics, MDPI, vol. 11(22), pages 1-33, November.
    5. de Faria, Haroldo & Costa, João Gabriel Spir & Olivas, Jose Luis Mejia, 2015. "A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 201-209.
    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. Olga Melnikova & Alexandr Nazarychev & Konstantin Suslov, 2022. "Enhancement of the Technique for Calculation and Assessment of the Condition of Major Insulation of Power Transformers," Energies, MDPI, vol. 15(4), pages 1-13, February.
    2. Sergio Bustamante & Mario Manana & Alberto Arroyo & Raquel Martinez & Alberto Laso, 2020. "A Methodology for the Calculation of Typical Gas Concentration Values and Sampling Intervals in the Power Transformers of a Distribution System Operator," Energies, MDPI, vol. 13(22), pages 1-18, November.
    3. Peters, Lennart & Madlener, Reinhard, 2017. "Economic evaluation of maintenance strategies for ground-mounted solar photovoltaic plants," Applied Energy, Elsevier, vol. 199(C), pages 264-280.
    4. Max Gabriel Steiner & Anderson Diogo Spacek & João Mota Neto & Pedro Rodrigo Silva Moura & Oswaldo Hideo Ando Junior & Cleber Lourenço Izidoro & Luciano Dagostin Bilessimo & Jefferson Diogo Spacek, 2020. "“In Situ” Evaluation of Mechanical Wear of Mobile Contacts of Electricity Voltage Regulator," Energies, MDPI, vol. 13(19), pages 1-17, September.
    5. George Kimani Irungu & Aloys Oriedi Akumu, 2020. "Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment," Energies, MDPI, vol. 13(18), pages 1-24, September.
    6. Zhi-Jun Li & Wei-Gen Chen & Jie Shan & Zhi-Yong Yang & Ling-Yan Cao, 2022. "Enhanced Distributed Parallel Firefly Algorithm Based on the Taguchi Method for Transformer Fault Diagnosis," Energies, MDPI, vol. 15(9), pages 1-22, April.
    7. Jelke Wibbeke & Payam Teimourzadeh Baboli & Sebastian Rohjans, 2022. "Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach," Energies, MDPI, vol. 15(9), pages 1-13, April.
    8. David L. Alvarez & Diego F. Rodriguez & Alben Cardenas & F. Faria da Silva & Claus Leth Bak & Rodolfo García & Sergio Rivera, 2021. "Optimal Decision Making in Electrical Systems Using an Asset Risk Management Framework," Energies, MDPI, vol. 14(16), pages 1-25, August.
    9. Raji Murugan & Ramasamy Raju, 2021. "Evaluation of in-service power transformer health condition for Inspection, Repair, and Replacement (IRR) maintenance planning in electric utilities," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(2), pages 318-336, April.
    10. Sun, YongTeng & Ma, HongZhong, 2024. "Research progress on oil-immersed transformer mechanical condition identification based on vibration signals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    11. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    12. Bing Zeng & Jiang Guo & Fangqing Zhang & Wenqiang Zhu & Zhihuai Xiao & Sixu Huang & Peng Fan, 2020. "Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition," Energies, MDPI, vol. 13(2), pages 1-20, January.
    13. Christian Gianoglio & Edoardo Ragusa & Andrea Bruzzone & Paolo Gastaldo & Rodolfo Zunino & Francesco Guastavino, 2020. "Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus," Energies, MDPI, vol. 13(5), pages 1-16, March.
    14. Qu, Guanghao & Li, Shengtao, 2023. "Atomic mechanisms of long-term pyrolysis and gas production in cellulose-oil composite for transformer insulation," Applied Energy, Elsevier, vol. 350(C).
    15. 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.
    16. Youcef Benmahamed & Omar Kherif & Madjid Teguar & Ahmed Boubakeur & Sherif S. M. Ghoneim, 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier," Energies, MDPI, vol. 14(10), pages 1-17, May.
    17. Hamid Mirshekali & Athila Q. Santos & Hamid Reza Shaker, 2023. "A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids," Energies, MDPI, vol. 16(17), pages 1-29, August.
    18. Tomasz Piotrowski & Pawel Rozga & Ryszard Kozak & Zbigniew Szymanski, 2020. "Using the Analysis of the Gases Dissolved in Oil in Diagnosis of Transformer Bushings with Paper-Oil Insulation—A Case Study," Energies, MDPI, vol. 13(24), pages 1-12, December.
    19. 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.
    20. 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.

    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:eee:rensus:v:199:y:2024:i:c:s1364032124001965. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.