Abstract
This article performs a thermal study through the application of Computational Fluid Dynamics (CFD) of a transformer prototype considering six different cooling configurations. Each configuration was computationally designed and simulated in the ANSYS CFX \(\copyright \) software. It was possible to analyze the temperature data of the hotspot, at the top oil and at the average temperature, thus comparing the thermal performance of each system and implementing the data in a simplified methodology for estimating the transformer lifespan standardized by IEEE C57.100-2011. In addition, the rendering of the results allows visualizing the temperature distribution and, consequently, the performance for each cooling system. Finally, the advantages and disadvantages of each cooling system were summarized, providing useful information for transformer designer’s decision. As some results, the forced air-cooled transformers have a thermal reduction of approximately 8.81% compared to natural air-cooled and the directed oil system transformers have superior cooling performance than forced oil and natural oil. The directed oil and forced air combination means a useful life 52.76% longer than the natural oil and natural air combination.
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Acknowledgements
An early version of paper was presented at XXIII Congresso Brasileiro de Automática (CBA 2020). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES/PROEX)–Finance Code 001 and in part by Companhia Paranense de Eletrificação (COPEL) through of P&D Program PD-06491-0421/2016. The authors would also to thank Federal University of Santa Maria (UFSM), UFSM Electrical Engineering Post-Graduation Program (PPGEE), UFSM Institute of Smart Grids and National Institute of Science and Technology-Distributed Generation (INCT-GD) by technical and financial support.
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Nogueira, G.C., Medeiros, L.H., Oliveira, M.M. et al. Thermal Analysis of Power Transformers with Different Cooling Systems Using Computational Fluid Dynamics. J Control Autom Electr Syst 33, 359–368 (2022). https://doi.org/10.1007/s40313-021-00848-0
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DOI: https://doi.org/10.1007/s40313-021-00848-0