Research on transformer temperature rise detection and optimization method based on multi-physical field coupling.

Area of Science:

• Electrical Engineering
• Thermal Management
• Transformer Testing

Background:

• Conventional temperature rise tests for dry-type transformers in box substations are inefficient.
• Accurate hotspot temperature monitoring is crucial for transformer performance and longevity.

Purpose of the Study:

• To develop an efficient and accurate methodology for dry-type transformer temperature rise testing.
• To reduce the time required for transformer testing while improving hotspot detection.

Main Methods:

• A closed-loop "Standard-Experiment-Simulation-Optimization" methodology was proposed.
• An electromagnetic-thermal coupled model was developed and calibrated with field data.
• A predictive model was created using initial test data to estimate steady-state conditions.

Main Results:

• The study identified a saturated exponential pattern in hotspot temperature rise.
• Additional thermocouple placements beyond standard positions improved early and accurate hotspot measurement.
• A predictive model accurately estimated steady-state temperature, rise value, and test duration using the first five hours of data.

Conclusions:

• The proposed methodology significantly reduces transformer testing time.
• The predictive model offers a reusable reference for enhancing hotspot detection accuracy.
• Optimized testing procedures contribute to improved dry-type transformer reliability.