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Influence of Cooling Management to Transformer Efficiency and Ageing

Author

Listed:
  • Zarko Janic

    (Končar Power Transformers, A Joint Venture of Siemens Energy and Končar, J. Mokrovića 12, HR-10090 Zagreb, Croatia)

  • Nebojsa Gavrilov

    (Končar Power Transformers, A Joint Venture of Siemens Energy and Končar, J. Mokrovića 12, HR-10090 Zagreb, Croatia)

  • Ivica Roketinec

    (Končar Power Transformers, A Joint Venture of Siemens Energy and Končar, J. Mokrovića 12, HR-10090 Zagreb, Croatia)

Abstract

Transformer efficiency is a key concern for both transformer operators and regulators, particularly with regards to new transformers. Cooling is a critical factor in preventing overheating and controlling the ageing of transformer insulation. This paper aims to analyse how different cooling setups can influence efficiency. Transformer losses depend on the winding temperature, which is affected by load, ambient temperature, and cooling power. In this study, we will take all of these factors into consideration to examine their impact on transformer losses. Using a 520 MVA transformer as an example, we will calculate the loss levels under different realistic ambient temperature and load profiles. We will also apply different cooling system settings to determine the optimization possibilities. Our calculations will consider different turn-on temperatures of the fans and cooling stages and will use steady state calculations over a period of 12 months. Additionally, a novel approach to control cooling by using the loading of the transformer is present, as are the possibilities of using variable speed fans. Finally, we will estimate the influence of cooling settings on transformer ageing by examining their impact on temperature.

Suggested Citation

  • Zarko Janic & Nebojsa Gavrilov & Ivica Roketinec, 2023. "Influence of Cooling Management to Transformer Efficiency and Ageing," Energies, MDPI, vol. 16(12), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4626-:d:1168120
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    References listed on IDEAS

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    1. Jayroop Ramesh & Sakib Shahriar & A. R. Al-Ali & Ahmed Osman & Mostafa F. Shaaban, 2022. "Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System," Energies, MDPI, vol. 15(21), pages 1-19, October.
    2. Marko Novkovic & Zoran Radakovic & Federico Torriano & Patrick Picher, 2023. "Proof of the Concept of Detailed Dynamic Thermal-Hydraulic Network Model of Liquid Immersed Power Transformers," Energies, MDPI, vol. 16(9), pages 1-26, April.
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    Cited by:

    1. Mohamed S. Seddik & Jehan Shazly & Magdy B. Eteiba, 2024. "Thermal Analysis of Power Transformer Using 2D and 3D Finite Element Method," Energies, MDPI, vol. 17(13), pages 1-23, June.
    2. Dong, Xiao-Jian & Shen, Jia-Ni & Liu, Cheng-Wu & Ma, Zi-Feng & He, Yi-Jun, 2024. "Simultaneous capacity configuration and scheduling optimization of an integrated electrical vehicle charging station with photovoltaic and battery energy storage system," Energy, Elsevier, vol. 289(C).

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