Research on Inversion Method of Internal Hot Spot Temperature in GIS Equipment and Influence of Sensor Distribution

ZHANG Shihan; CHEN Xiaoxin; LI Feiran; CAO Hangyu; LUO Lingen; SHENG Gehao

doi:10.13296/j.1001-1609.hva.2025.10.011

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Research on Inversion Method of Internal Hot Spot Temperature in GIS Equipment and Influence of Sensor Distribution

Digital Twin Technology and Applications for Multi-scenario in New Power System | 更新时间：2025-10-21

- Research on Inversion Method of Internal Hot Spot Temperature in GIS Equipment and Influence of Sensor Distribution
- High Voltage Apparatus Vol. 61, Issue 10, Pages: 94-105(2025)
- 作者机构：
- 作者简介：
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- DOI：10.13296/j.1001-1609.hva.2025.10.011
  CLC：
- Received：21 March 2025，
  
  Revised：2025-07-02，
  
  Published：16 October 2025
- 稿件说明：
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ZHANG Shihan, CHEN Xiaoxin, LI Feiran, et al. Research on Inversion Method of Internal Hot Spot Temperature in GIS Equipment and Influence of Sensor Distribution[J]. High voltage apparatus, 2025, 61(10): 94-105.
DOI：

ZHANG Shihan, CHEN Xiaoxin, LI Feiran, et al. Research on Inversion Method of Internal Hot Spot Temperature in GIS Equipment and Influence of Sensor Distribution[J]. High voltage apparatus, 2025, 61(10): 94-105. DOI： 10.13296/j.1001-1609.hva.2025.10.011.

摘要

气体绝缘金属封闭开关设备的母线温升直接影响设备的可靠性，其热点温度的推演对设备安全稳定运行具有重要意义。文中基于真型三相共筒GIS构建电磁场—流场—温度场的多物理场耦合仿真模型，搭建GIS母线温升试验平台以验证仿真模型的准确性。在此基础上，利用机器学习算法对仿真数据进行训练并反演GIS母线内部热点温度。将温升试验中的外壳温度数据作为反演网络的输入，通过对比输出的母线反演温度值与实测值来验证反演的有效性与准确性，为基于仿真的GIS内部热故障诊断及预测提供可能。此外，还对比分析了不同外壳测温点组合的反演结果，给出了优化测温点布置方案以指导实际应用。研究结果表明，外壳轴向测温点对于内部温度反演的影响明显大于径向测温点，而轴向测温点中，上方测温点对反演准确率影响最大。该结论可进一步提高GIS运行状态监测和热故障诊断的准确性，优化测温点布局，提升检修效率。

Abstract

The temperature rise of busbar of gas-insulated switchgear (GIS) directly affects the reliability of the equipment

and the calculation of its hot spot temperature is of great significance for the safe and stable operation of the equipment. In this study

a multi-physics coupled simulation model of the electromagnetic

flow

and thermal fields is developed based on a three-phase enclosedGIS. A GISbus temperature rise test platform is set up to verify the correctness of the simulation model .On this basis

the machine learning algorithms are used to train the simulation data and invert the hot spot temperature inside the GIS bus. The temperature data of enclosure from the temperature rise test is taken as input to the inversion network

the effectiveness and accuracy of the inversion are verified by comparing the output inversion temperature value with the measured value

thus providing a possibility for internal thermal fault disgnosis and prediction. Additionally

the inversion effect of different combinations of temperature measurement points of enclosure are compared and analyzed

and the layout of the optial temperature measurement point to guide the practical application.The research results show that the influence of axial temperature measurement points on internal temperature inversion of the enclosure is significantly greater than that of radial temperature measurement points

while among the axial temperature measurement points

the upper measurement point has the greatest impact on the inversion accuracy. The conclusion can enhance the accuracy of GIS status monitoring and fault diagnosis

optimize temperature measurement point layouts and improve maintenance efficiency.

关键词

Keywords

references

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