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<p>Accurately estimating hotspot temperatures in UHV converter transformers is challenging due to the strong nonlinearity of oil properties, harmonic losses, and thermal–fluid interactions—difficulties that become significantly amplified under extremely cold conditions. This study addresses these challenges by developing a high-fidelity, physics-informed flow–thermal model that, for the first time, explicitly incorporates extreme-cold oil behaviour, temperature-dependent viscosity, and harmonic-dependent heat generation. The resulting two-dimensional refined model captures narrow oil ducts, complex cooling paths, and strongly temperature-sensitive fluid properties, enabling realistic reproduction of ± 800&#xa0;kV winding thermal behaviour across − 40&#xa0;°C to 40&#xa0;°C. A total of 12,792 physically consistent hotspot samples are generated through extensive parametric simulations. To characterize the temporal evolution of hotspot temperatures, a GRU model is constructed and its hyperparameters optimized using TPE. The proposed TPE-GRU achieves a mean absolute error of 1.21&#xa0;°C and maintains maximum deviation below 1.1&#xa0;°C under extreme cold, outperforming BPNN, SVR, and baseline GRU models. With an inference time of 1.2s, the method enables real-time hotspot estimation for cold-climate transformer operation.</p>

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A physics–informed TPE-GRU framework for fast and accurate hotspot temperature estimation of UHV oil-immersed transformers in extremely cold regions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月19日

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  • Kunhan Wang,
  • Yonglin Pang,
  • Yuwei Dai,
  • Tianqi Liu,
  • Yuchen Huang

摘要

Accurately estimating hotspot temperatures in UHV converter transformers is challenging due to the strong nonlinearity of oil properties, harmonic losses, and thermal–fluid interactions—difficulties that become significantly amplified under extremely cold conditions. This study addresses these challenges by developing a high-fidelity, physics-informed flow–thermal model that, for the first time, explicitly incorporates extreme-cold oil behaviour, temperature-dependent viscosity, and harmonic-dependent heat generation. The resulting two-dimensional refined model captures narrow oil ducts, complex cooling paths, and strongly temperature-sensitive fluid properties, enabling realistic reproduction of ± 800 kV winding thermal behaviour across − 40 °C to 40 °C. A total of 12,792 physically consistent hotspot samples are generated through extensive parametric simulations. To characterize the temporal evolution of hotspot temperatures, a GRU model is constructed and its hyperparameters optimized using TPE. The proposed TPE-GRU achieves a mean absolute error of 1.21 °C and maintains maximum deviation below 1.1 °C under extreme cold, outperforming BPNN, SVR, and baseline GRU models. With an inference time of 1.2s, the method enables real-time hotspot estimation for cold-climate transformer operation.