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A Surface Thermal Sensing Framework for Internal Winding Temperature Estimation in Oil-Immersed Converter
Sheng Han1, Zhiqiang Wang1, Yuchen Tang2
1Key Laboratory of Cleaner Intelligent Control on Coal & Electricity, Taiyuan University of Technology, Taiyuan 030600, China.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
A novel non-invasive method estimates internal winding temperature in oil-immersed transformers using surface sensors and hybrid deep learning (XGBoost-LSTM). This approach offers accurate, real-time thermal monitoring for improved operational reliability.
Area of Science:
- Electrical Engineering
- Thermal Management
- Artificial Intelligence
Background:
- Accurate internal winding temperature monitoring is crucial for oil-immersed transformer reliability.
- Direct internal sensor deployment is challenging due to insulation and structural limitations.
Purpose of the Study:
- To develop a non-invasive method for estimating internal winding temperature.
- To enhance thermal state assessment and operational reliability of transformers.
Main Methods:
- Utilized surface temperature sensing combined with a hybrid deep learning model (XGBoost-LSTM).
- Employed XGBoost for optimal sensor location selection based on correlation with internal temperature.
- Applied LSTM to model temporal temperature dynamics using selected surface data.
Main Results:
- The XGBoost-LSTM model achieved internal winding temperature estimation with an error below 1.5 K.
- Validated through numerical simulations and experimental testing on a scaled transformer model.
- Demonstrated a non-invasive and sensor-efficient alternative to direct internal sensing.
Conclusions:
- The proposed non-invasive method effectively estimates internal winding temperature.
- Offers a viable solution for real-time thermal state estimation and condition monitoring.
- Has significant potential for fault diagnosis in oil-immersed converter transformers.
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