3D Frequency Domain Reflectometry Digital Twin of an Electrical Cable: A First Glance

被引:1
作者
Spencer, Mychal P. [1 ]
Sriraman, Aishwarya [1 ]
Glass, Bill [1 ]
Fifield, Leonard S. [1 ]
机构
[1] Pacif Northwest Natl Lab, Richland, WA 99354 USA
来源
2022 IEEE CONFERENCE ON ELECTRICAL INSULATION AND DIELECTRIC PHENOMENA (IEEE CEIDP 2022) | 2022年
关键词
Digital twin; electrical cable; insulation; FDR;
D O I
10.1109/CEIDP55452.2022.9985387
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
Electrical cables within nuclear power plants (NPPs) are critical components required for power, control, and instrumentation systems which may be exposed to stressors, such as elevated temperatures and gamma radiation. Such stressors can lead to a reduction in the remaining useful life of electrical cables, jeopardizing the safety of NPP systems. To evaluate the effect of stressors on the degradation of electrical cables, electrical reflectometry methods are commonly employed. Frequency domain reflectometry (FDR) is a non-destructive electrical reflectometry method that uses transmission line theory to detect degradation or impedance changes within electrical cables. However, in most cases FDR is only applied to de-energized cables, limiting the application in NPPs as the cable system must be taken offline. In this work, we explore the development of an FDR digital twin to predict the degradation of an electrical cable exposed to elevated temperature, which is expected to reduce the need for offline FDR. A 3-conductor low-voltage electrical cable was selected for evaluation of the digital twin. The fully three-dimensional digital twin was developed in COMSOL using the radio frequency module. A cable length of 30-m and frequency bandwidth of 400 MHz was selected to mimic real-world application of FDR. Over a 1-m region, the permittivity of the insulation was varied by up to 20% to model thermal degradation. The results demonstrate accurate detection of the insulation damage region, supporting further investigation of the FDR digital twin using real-world data and machine learning for predictive damage estimation or remaining lifetime.
引用
收藏
页码:103 / 106
页数:4
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