Enterprise AI Analysis
A physics-informed TPE-GRU framework for fast and accurate hotspot temperature estimation of UHV oil-immersed transformers in extremely cold regions
Authors: Kunhan Wang¹, Yonglin Pang¹, Yuwei Dai¹, Tianqi Liu*, and Yuchen Huang²
Publication: Wang et al. Journal of Engineering and Applied Science (2026) 73:23
Abstract: 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.
Executive Impact
Unlock Unprecedented Efficiency in Transformer Thermal Management
This research delivers a transformative approach to real-time hotspot temperature estimation for UHV transformers operating in extreme cold, offering significant advancements in predictive maintenance and operational reliability.
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Mean Absolute Error
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Faster than FEM Simulation
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Hotspot Samples Generated
Deep Analysis & Enterprise Applications
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Physics-informed Modeling for UHV Transformers
This study introduces a high-fidelity, physics-informed flow-thermal model specifically designed for UHV converter transformers. Unlike traditional approaches, it explicitly accounts for extreme-cold oil behavior, temperature-dependent viscosity, and harmonic-dependent heat generation, which are critical factors in harsh environments.
Extreme Cold Operations
Critical Challenge Addressed
TPE-GRU for Enhanced Predictive Accuracy
To overcome the computational cost of detailed simulations, a Gated Recurrent Unit (GRU) model, optimized via a Tree-structured Parzen Estimator (TPE), is employed. This framework captures the complex temporal evolution of hotspot temperatures from a large dataset of physically consistent samples, ensuring both accuracy and generalization.
Enterprise Process Flow
Physics-informed Flow-Thermal Modeling
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Parametric Simulations & Data Generation (12,792 samples)
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GRU Model Construction & TPE Optimization
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Real-time Hotspot Estimation
Superior Performance in Harsh Environments
The proposed TPE-GRU model demonstrates significantly improved performance over traditional methods, delivering high accuracy and robustness even under extreme cold conditions, making it ideal for real-time monitoring and predictive maintenance in critical infrastructure.
1.21 °C
Mean Absolute Error (MAE)
| Model | Mean Absolute Error (°C) | Inference Time (s) | Robustness in Extreme Cold |
|---|---|---|---|
| TPE-GRU (Proposed) | 1.21 | 1.2 | High (max deviation < 1.1°C) |
| BPNN | Higher | Much Slower | Moderate |
| SVR | Highest | Slow | Low |
| Baseline GRU | Higher | Slower | Good |
7500x
Speed Improvement over FEM Simulation
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating this advanced AI solution into your operations, designed for maximum impact and minimal disruption.
Phase 1: Foundation (2-4 Weeks)
Develop high-fidelity 2D flow-thermal models incorporating extreme-cold oil behavior and harmonic losses. Collect and validate initial operational parameters.
Phase 2: Data Generation & Preprocessing (4-8 Weeks)
Generate 12,792 physically consistent hotspot samples under varying loads and ambient temperatures. Normalize input features and apply temporal smoothing.
Phase 3: Model Development & Optimization (6-12 Weeks)
Construct GRU network for temporal prediction. Use Tree-structured Parzen Estimator (TPE) for hyperparameter optimization to enhance accuracy and generalization.
Phase 4: Validation & Deployment (3-6 Weeks)
Validate model accuracy and robustness under extreme cold conditions. Deploy for real-time hotspot estimation, dynamic rating, and predictive maintenance.
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