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| Rapid Prediction Method for Temperature Rise and Heat Flow in High-Voltage AC Vacuum Circuit Breakers Using Thermal Network Model |
| Huang Lei, Ma Hui, Xu Wendi, Geng Yingsan, Liu Zhiyuan |
| School of Electrical Engineering Xi’an Jiaotong University Xi’an 710049 China |
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Abstract The temperature rise characteristics of electrical equipment directly affect its operational stability and service life. Prolonged high temperatures in localized areas can lead to a gradual decline in insulation performance, potentially causing equipment failure or damage. Therefore, understanding the equipment’s temperature rise and heat flow characteristics is crucial for improving its reliability. However, it is difficult to directly measure internal temperature data in closed or complex electrical equipment. When using numerical simulation methods, one faces challenges such as complex modeling and long computation times that hinder the acquisition of practical temperature data.
This paper proposes a thermal network model with self-calibration capabilities that combines physical mechanisms and data-driven algorithms to calibrate the parameters of thermal network components. It enables rapid prediction of temperature rise and heat flow along heat dissipation paths in high-voltage AC vacuum circuit breakers (VCBs) as a function of load current, thereby providing practical support for structural optimization during the design phase and for condition monitoring during operational phases. First, temperature data at different load currents were obtained from multiphysics numerical simulations, and a lumped-parameter thermal network was designed based on these data and the heat-transfer processes. Subsequently, based on theoretical formulas for heat sources and thermal resistance, the paper derives corresponding correction functions and optimizes the correction component using ridge regression and genetic algorithms. It enables self-calibration of parameters during iterative calculations, addressing variations in component parameters with temperature and load current within the thermal network.
Model accuracy was validated under 1 575 A and 3 000 A load scenarios, focusing on temperature rise at critical nodes and heat flow along heat-dissipation paths. On a workstation equipped with an Intel Xeon Gold 5218R processor and 128 GB RAM, steady-state multi-physics simulations in ANSYS Workbench 2021b required approximately 8h per operating condition, whereas the thermal network model implemented in Matlab/Simulink 2021a completed the same case in approximately 1 min, yielding an efficiency improvement of about two orders of magnitude. Average prediction errors were 3.7% (3.7 K) for temperature rise and 5.5% (4.3 W) for heat flow.
Accordingly, the proposed rapid-prediction approach enables efficient and accurate evaluation across load conditions, substantially benefiting the thermal design, prediction, and assessment of high-voltage AC vacuum circuit breakers. Incorporating parameter self-calibration further improved accuracy: the mean temperature-rise error decreased from 13.6% (uncalibrated) to 5.7% (self-calibrated), a 58% reduction. At nodes sensitive to radiative thermal resistance (5, 6, 8, 9, 10), the mean error fell from 24.4% (maximum 50.0%) to 8.0%, with all errors ≤15%, indicating a marked reduction in local deviations.
The thermal network model with self-calibration capabilities can effectively predict the temperature rise and heat flow along heat dissipation paths in a high-voltage AC VCB. Future research will extend the application of this model to transient analysis and incorporate deep learning techniques to enhance prediction accuracy. This model is not only applicable to VCBs but also provides theoretical support and technical reference for the thermal management system design of other electrical equipment.
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Received: 08 April 2025
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2026, Vol. 41 
