Research on High Voltage Cable Core Temperature Prediction Based on Data Driven Model

doi:10.3969/j.issn.1008-0198.2022.02.011

Abstract

Abstract: Temperature is one of the important physical quantities to determine the operating status of the cable, but usually only the temperature of the outer sheath can be monitored, while the temperature of the cable core cannot be directly obtained. In this study, a temperature field model of tunnel laying 110 kV cable is built, incorporating the real-time monitoring temperature of the cable outer sheath. The cable temperature distribution is thus obtained by optimizing and correcting the physical quantities such as material parameters of the cable to make the model more consistent with the real operation state. The influences of parameters such as dielectric loss factor, dielectric constant and thermal conductivity of XLPE on the physical model of cable temperature prediction are further discussed. The temperature distribution of low resistance grounding fault in cable is simulated. It was found that the difference between the fault phase cable core temperature and its outer sheath temperature was 47 ℃, and this cable core temperature can reach 250 ℃, and meanwhile, the temperature of the same circuit non-fault phase cable core also rose to more than 120 ℃, which was in severe overload state. The data-driven high voltage cable model in this paper can effectively calculate the cable core temperature, which helps to grasp the core data and working status of cable operation,so that it can lay the foundation for the final construction of the cable digital twin model.

Key words: 110 kV cable, cable temperature field, cable core temperature, tunnel laying cable model, digital twin

CLC Number:

TM247

CHEN She, WANG Juan, ZENG Zeyu, ZHOU Pan. Research on High Voltage Cable Core Temperature Prediction Based on Data Driven Model[J]. Hunan Electric Power, 2022, 42(2): 59-63.

References

[1] 李凤龙,周玉勇,赵东杰,等.基于特高压直流输电工程数字孪生应用探索[J].电气时代,2021(6): 24-25,28.
[2] Batty M. Digital twins[J]. Environment and Planning B: Urban Analytics and City Science, 2018, 45(5): 817-820.
[3] Jiang Y, Yin S, Li K, et al. Industrial applications of digital twins[J]. Philosophical Transactions of the Royal Society A, 2021, 379(2207): 20200360.
[4] Tao F, Zhang H, Liu A, et al. Digital twin in industry: State-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2018, 15(4): 2405-2415.
[5] 陈铭,周丹婷.变压器多维状态感知及数字孪生技术应用研究[J].电子技术与软件工程,2021(15): 222-223.
[6] 吴学正,李树荣.基于数字孪生的GIS智能变电站健康评估及故障诊断模型[J].河北电力技术,2021,40(3): 15-18,48.
[7] 王浩,许海伟,杜勇,等.基于数字孪生模型的GIS筒体关键部件温变行为研究[J].高电压技术,2021,47(5): 1584-1594.
[8] 朱江. 基于多场耦合的电力电缆温度场仿真及其监测系统研究[D].北京:华北电力大学,2017.
[9] 王雅妮,张洪亮,吴建东,等.不同敷设方式下高压直流电缆温度场与电场仿真计算研究[J].绝缘材料,2017,50(7): 71-78.
[10] 郑照勇. 大截面交联聚乙烯电缆管道敷设力学分析及其载流量计算[D].哈尔滨:哈尔滨理工大学,2019.
[11] 郭少锋. 隧道敷设多回高压电缆多物理场结合电路仿真及其布置优化的研究[D].广州:华南理工大学,2019.
[12] 杜林,余辉宗,严涵.基于构建温度场分析的非侵入式高压电缆缆芯温度测量方法[J].电工技术学报,2021,36(7): 1338-1346.
[13] 陈伟. 110kV电力电缆本体及中间接头温度场分布研究[D].宜昌:三峡大学,2020.
[14] 郭旭,王青山,何星躲,等.配电网电缆导体温度趋势仿真与系统设计[J].供用电,2020,37(10): 83-88.
[15] Wang Y, Zhao A, Zhang X, et al. Study of dielectric response characteristics for thermal aging of XLPE cable insulation[C]//2016 International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2016: 602-605.
[16] Crine J P. Electrical aging and breakdown of crosslinked polyethylene cables[C]//Annual Report Conference on Electrical Insulation and Dielectric Phenomena. IEEE, 2002: 23-26.
[17] 李应宏,雷成华,刘刚.基于材料导热系数的单芯高压电缆导体温度动态分析[J].华东电力,2011,39(8): 1299-1303.