Research on Finite Element Reduced Order Modeling Method of Transformer Temperature Field for Digital Twin Application

被引:0
|
作者
Jing L. [1 ]
Dong X. [1 ]
Yang C. [2 ]
Fan W. [2 ,3 ]
Li T. [2 ,3 ]
Wang L. [1 ]
机构
[1] School of Electric Power, Shenyang Institute of Engineering, Shenyang
[2] State Grid Liaoning Electric Power Co., Ltd., Shenyang
[3] Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang
来源
关键词
digital twin model; proper orthogonal decomposition (POD); reduced order model; transformer temperature field; transient fluid solid coupling;
D O I
10.13336/j.1003-6520.hve.20220689
中图分类号
学科分类号
摘要
In order to solve the transient fluid solid coupling temperature field of oil immersed transformer, the digital twin reduced order model of transformer is constructed, which can quickly solve the transient temperature field. Firstly, the digital twin implementation architecture of transformer based on internet of things (IoT) is established, and the full order Galerkin finite element model of transient fluid solid coupling temperature field is built. Secondly, a reduced order model of transient temperature field is established by combining proper orthogonal decomposition (POD) with finite element method, and the digital twin modeling and reduced order calculation flow are given based on the measured data. Finally, the actual transformer temperature rise test is carried out to ensure the accuracy of the full order model; meanwhile, the temperature field distribution is quickly calculated by using different order reduced models, and the calculation error and time of each order model are compared. The results show that the temperature rise error between the calculated value and the measured value is with in 1.5 ℃. The results of the third-order model and the full order model meet the requirements of POD error specification. Compared with the full order model, the computational time of the reduced order model is reduced from hours-level to seconds-level. The research results verify the accuracy and timeliness of the reduced order model, which can ensure the accuracy of the digital twin model and maximize the efficiency of the solution. © 2023 Science Press. All rights reserved.
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页码:2408 / 2419
页数:11
相关论文
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