Reverse Oil Flow Characterization in Transformer Windings: A Fluid-Thermal Network Approach

Lujia Wang (), Jianghao Qi, Yifan Chen, Lebin Zhang and Jianwen Zhang
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Lujia Wang: School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Jianghao Qi: School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Yifan Chen: School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Lebin Zhang: School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Jianwen Zhang: School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China

Energies, 2025, vol. 18, issue 7, 1-23

Abstract: When the inlet flow velocity in the disc-type winding region of an oil-immersed transformer operates within a high Reynolds number range, it leads to an uneven distribution of oil flow. This phenomenon results in the abnormal occurrence of reverse oil flow in the bottom oil ducts, causing the hotspot temperature to rise instead of decrease. To address this issue, a three-node flow resistance module was introduced at the intersection of T-shaped oil ducts based on the flow paths of oil in the main and branch ducts within the disc-type winding region. A flow network model for the transformer winding region was subsequently constructed. The accuracy of the model was validated through CFD simulations and experiments conducted on a transformer winding region test platform, with a maximum relative error of 4.02%. The model successfully predicted the flow distribution of the cooling oil within the winding region. Furthermore, by considering the structural characteristics of the winding region and the principles of heat transfer, particular attention was given to variations in local Nusselt number correlations. This led to the development of a thermal network model tailored to the winding region experiencing reverse oil flow. Comparative analysis of the model’s calculation results yielded a maximum relative error of only 1.12%, demonstrating its ability to rapidly and accurately elucidate the reverse oil flow effect. This study provides a theoretical foundation for the identification and mitigation of reverse oil flow in future applications.

Keywords: pancake transformer winding; reverse oil flow; flow network model; thermal network model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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