Research on temperature distribution characteristics of oil-immersed power transformers based on fluid network decoupling

被引:0
|
作者
Xu, Yongming [1 ]
Xu, Ziyi [1 ]
Ren, Congrui [2 ]
Wang, Yaodong [3 ]
机构
[1] Changzhou Inst Technol, Sch Elect & Informat Engn, Changzhou 213032, Peoples R China
[2] Harbin Univ Sci & Technol, Sch Elect & Elect Engn, Harbin, Peoples R China
[3] Univ Durham, Durham Coll, Durham, England
来源
HIGH VOLTAGE | 2024年 / 9卷 / 05期
基金
中国国家自然科学基金;
关键词
HOT-SPOT; MODEL; PREDICTION;
D O I
10.1049/hve2.12488
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Due to the complex structure and large size of large-capacity oil-immersed power transformers, it is difficult to predict the winding temperature distribution directly by numerical analysis. A 180 MVA, 220 kV oil-immersed self-cooling power transformer is used as the research object. The authors decouple the internal fluid domain of the power transformer into four regions: high voltage windings, medium voltage windings, low voltage windings, and radiators through fluid networks and establish the 3D fluid-temperature field numerical analysis model of the four regions, respectively. The results of the fluid network model are used as the inlet boundary conditions for the 3D fluid-temperature numerical analysis model. In turn, the fluid resistance of the fluid network model is corrected according to the results of the 3D fluid-temperature field numerical analysis model. The prediction of the temperature distribution of windings is realised by the coupling calculation between the fluid network model and the 3D fluid-temperature field numerical analysis model. Based on this, the effect of the loading method of the heat source is also investigated using the proposed method. The hotspot temperatures of the high-voltage, medium-voltage, and low-voltage windings are 89.43, 86.33, and 80.96 degrees C, respectively. Finally, an experimental platform is built to verify the results. The maximum relative error between calculated and measured values is 4.42%, which meets the engineering accuracy requirement.
引用
收藏
页码:1136 / 1148
页数:13
相关论文
共 50 条
  • [21] Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers
    Wang, Chen
    Wu, Jie
    Wang, Jianzhou
    Zhao, Weigang
    ENERGIES, 2016, 9 (01)
  • [22] Fiber Bragg Grating Sensors for Temperature monitoring in Oil-Immersed Transformers
    Jiang, Yi
    Liu, Shuang
    Xiao, Li
    Li, Wei
    2016 15TH INTERNATIONAL CONFERENCE ON OPTICAL COMMUNICATIONS AND NETWORKS (ICOCN), 2016,
  • [23] NONLINEAR THERMAL MODELING OF INDOOR AND OUTDOOR OIL-IMMERSED POWER TRANSFORMERS
    Iskender, Ires
    Mamizadeh, Ali
    JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS, 2009, 60 (06): : 321 - 327
  • [24] Oil-immersed power transformer internal fault diagnosis research based on probabilistic neural network
    Yu, Shenghao
    Zhao, Dongming
    Chen, Wei
    Hou, Hui
    7TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2016) / THE 6TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2016) / AFFILIATED WORKSHOPS, 2016, 83 : 1327 - 1331
  • [25] Experimental Research on the Characteristics of Radiator Batteries of Oil Immersed Power Transformers
    Rogora, Dario
    Nazzari, Stefano
    Radoman, Uros
    Radakovic, Zoran R.
    IEEE TRANSACTIONS ON POWER DELIVERY, 2020, 35 (02) : 725 - 734
  • [26] Effects of Metal Passivator Degradation on the Dissolved Gases Characteristics of Oil in Oil-immersed Transformers
    Gao, Si-hang
    Zeng, Xi-Song
    Zhang, Guo-wen
    IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION, 2021, 28 (05) : 1735 - 1742
  • [27] Research developments of panel type radiators cooling oil-immersed power transformers based on energy-saving materials
    Xing, Yunlong
    Jin, Ying-ai
    Che, Xianda
    Liu, Jun
    Gao, Qing
    ADVANCED RESEARCH ON STRUCTURE, MATERIALS AND ENGINEERING II, 2013, 700 : 243 - 246
  • [28] Multiparameter-Based Fuzzy Logic Health Index Assessment for Oil-Immersed Power Transformers
    Mharakurwa, Edwell Tafara
    Goboza, Rutendo
    ADVANCES IN FUZZY SYSTEMS, 2019, 2019
  • [29] Health status perception of oil-immersed power transformers considering wind power uncertainty
    Yi, Lingzhi
    Su, Xingren
    Wang, Yahui
    Xu, Xunjian
    Liu, Jiangyong
    She, Haixiang
    ELECTRIC POWER SYSTEMS RESEARCH, 2024, 234
  • [30] Fault diagnosis of oil-immersed power transformers using kernel based extreme learning machine
    Zhang, Liwei
    Metallurgical and Mining Industry, 2015, 7 (07): : 213 - 218