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A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis

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  • de Faria, Haroldo
  • Costa, João Gabriel Spir
  • Olivas, Jose Luis Mejia

Abstract

Electric power transformers are the link between the generators of a power system and the transmission lines and between lines of different voltage levels. Power transformers undergo changes in their operational life expectancy and reliability over the years. Currently, several tools for diagnosis and assessment of their operational condition are available, including diagnostic techniques based on dissolved gas analysis in the insulating oil. Through monitoring of dissolved gases in oil, it is possible to perform detailed data analysis, seeking systemic failure prediction. The adoption of new technologies for maintenance of power transformers can induce substantial changes in the reliability of such equipment in view of the existence of a global trend to decrease operational costs, predict maintenances and control substations in a centralized way. This paper describes the main factors that lead to lifetime reduction in transformers and reviews the main methods used for predictive maintenance based on dissolved gas analysis. The advantages and disadvantages of each one are outlined and some future directions for research are proposed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:46:y:2015:i:c:p:201-209
    DOI: 10.1016/j.rser.2015.02.052
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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. Manito, Alex R.A. & Pinto, Aimé & Zilles, Roberto, 2016. "Evaluation of utility transformers' lifespan with different levels of grid-connected photovoltaic systems penetration," Renewable Energy, Elsevier, vol. 96(PA), pages 700-714.
    9. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    10. 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.
    11. 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.
    12. 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).
    13. Bing Zeng & Jiang Guo & Wenqiang Zhu & Zhihuai Xiao & Fang Yuan & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM," Energies, MDPI, vol. 12(21), pages 1-18, November.
    14. Xiaojun Tang & Wenjing Wang & Xuliang Zhang & Erzhen Wang & Xuanjiannan Li, 2018. "On-Line Analysis of Oil-Dissolved Gas in Power Transformers Using Fourier Transform Infrared Spectrometry," Energies, MDPI, vol. 11(11), pages 1-15, November.
    15. 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.

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