A novel approach to building digital twin transformers by combining virtual-real sensing: An example of degree of polymerization distribution

被引:8
作者
Luo, Hao [1 ]
Cheng, Li [1 ]
Yang, Lijun [1 ]
Zhao, Xuetong [1 ]
Liao, Ruijin [1 ]
Zhang, Yongze [2 ]
机构
[1] Chongqing Univ, Sch Elect Engn, State Key Lab Power Transmiss Equipment Technol, Chongqing 400044, Peoples R China
[2] Xian XD Transformer Co Ltd, Xian 710077, Peoples R China
关键词
Virtual-real sensing (VRS); Transformer; Digital twin (DT); Distribution of degree of polymerization (DP); VIBRATION; SENSOR; PAPER;
D O I
10.1016/j.measurement.2023.113714
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Digital twin (DT) in the power industry faces challenges due to complex structures and limited distributed sensors for power equipment. In this work, a novel approach combining virtual-real sensing (VRS) was used to build DT for transformers, taking the degree of polymerization (DP) distribution as an example. Temperature and moisture distributions were calculated, and a dynamic deduction model for DP distribution was established. The visualization of DP distribution was achieved through surface-rebuild. Results showed a 3.5% error in temperature distribution compared to monitoring data. Moisture distribution was concentrated in the cardboard and bottom solid insulation, reaching 3.2% in the 48th year. DP distribution pattern aligned with Cigre's report, with the lowest value at the hot spot location of the LV windings, deduced to be 252.15 in the 48th year. This study provides a data base for realizing transformers DT and is a key step towards building a vital DT transformer.
引用
收藏
页数:12
相关论文
共 39 条
[1]  
ABB, 2018, China electromechanical industry, V24, P24
[2]   Digital twin real-time hybrid simulation platform for power system stability [J].
Abo-Khalil, Ahmed G. .
CASE STUDIES IN THERMAL ENGINEERING, 2023, 49
[3]   On the Feasibility of Monitoring Power Transformer's Winding Vibration and Temperature along with Moisture in Oil Using Optical Sensors [J].
Akre, Simplice ;
Fofana, Issouf ;
Yeo, Zie ;
Brettschneider, Stephan ;
Kung, Peter ;
Sekongo, Bekibenan .
SENSORS, 2023, 23 (04)
[4]   Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating [J].
Bado, Mattia Francesco ;
Tonelli, Daniel ;
Poli, Francesca ;
Zonta, Daniele ;
Casas, Joan Ramon .
SENSORS, 2022, 22 (09)
[5]  
Coutinho C.P., 2020, Transformer 4.0 - Digital Revolution of Power Transformers
[6]   On the degradation evolution equations of cellulose [J].
Ding H.-Z. ;
Wang Z.D. .
Cellulose, 2008, 2 (205-224) :205-224
[7]   Significance and Detection of Very Low Degree of Polymerization of Paper in Transformers [J].
Duval, Michel ;
de Pablo, Alfonso ;
Atanasova-Hoehlein, Ivanka ;
Grisaru, Marius .
IEEE ELECTRICAL INSULATION MAGAZINE, 2017, 33 (01) :31-38
[8]   Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties [J].
Elsisi, Mahmoud ;
Minh-Quang Tran ;
Mahmoud, Karar ;
Mansour, Diaa-Eldin A. ;
Lehtonen, Matti ;
Darwish, Mohamed M. F. .
MEASUREMENT, 2022, 190
[9]   High-Sensitivity Fiber-Optic Sensor for Hydrogen Detection in Gas and Transformer Oil [J].
Fisser, Maximilian ;
Badcock, Rodney A. ;
Teal, Paul D. ;
Hunze, Arvid .
IEEE SENSORS JOURNAL, 2019, 19 (09) :3348-3357
[10]   A Multiphysical Model to Study Moisture Dynamics in Transformers [J].
Garcia, Belen ;
Villarroel, Rafael ;
Garcia, Diego .
IEEE TRANSACTIONS ON POWER DELIVERY, 2019, 34 (04) :1365-1373