Modeling of high-temperature superconducting cable via time domain reflectometry and general regression neural network

Gu Young Kwon; Su Sik Bang; Yeong Ho Lee; Geon Seok Lee; Yong June Shin

doi:10.1109/TASC.2019.2898885

Modeling of high-temperature superconducting cable via time domain reflectometry and general regression neural network

Gu Young Kwon, Su Sik Bang, Yeong Ho Lee, Geon Seok Lee, Yong June Shin

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

When a fault occurs in the high-temperature superconducting (HTS) cable, a fault current consisting of extremely high frequency components is generated and propagated based on the transient response of the HTS cable. Therefore, a simulation model based on high frequency characteristic of the HTS cable is essential to obtain accurate simulation results in the transient fault analysis. In this paper, the result of time domain reflectometry is used to design the model of the HTS cable. To determine model parameters, a general regression neural network based on the kernel density estimation is utilized. After the modeling procedure, the accuracy of the model is evaluated by time-frequency domain reflectometry, whose response depends on the high frequency characteristic of the cable. It is expected that the proposed modeling method can be applied to various application area of HTS cable, such as fault analysis, protection, and diagnostics in the future.

Original language	English
Article number	8641370
Journal	IEEE Transactions on Applied Superconductivity
Volume	29
Issue number	5
DOIs	https://doi.org/10.1109/TASC.2019.2898885
Publication status	Published - 2019 Aug

Bibliographical note

Funding Information:
Manuscript received October 29, 2018; accepted February 5, 2019. Date of publication February 13, 2019; date of current version March 13, 2019. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning, #NRF-2017R1A2A1A05001022. This work was also supported under the framework of international cooperation program managed by the National Research Foundation of Korea (2016K2A9A1A03905116). (Corresponding author: Yong-June Shin.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:,yongjune@yonsei.ac.kr).

Publisher Copyright:
© 2002-2011 IEEE.

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Electrical and Electronic Engineering

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10.1109/TASC.2019.2898885

Cite this

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title = "Modeling of high-temperature superconducting cable via time domain reflectometry and general regression neural network",

abstract = "When a fault occurs in the high-temperature superconducting (HTS) cable, a fault current consisting of extremely high frequency components is generated and propagated based on the transient response of the HTS cable. Therefore, a simulation model based on high frequency characteristic of the HTS cable is essential to obtain accurate simulation results in the transient fault analysis. In this paper, the result of time domain reflectometry is used to design the model of the HTS cable. To determine model parameters, a general regression neural network based on the kernel density estimation is utilized. After the modeling procedure, the accuracy of the model is evaluated by time-frequency domain reflectometry, whose response depends on the high frequency characteristic of the cable. It is expected that the proposed modeling method can be applied to various application area of HTS cable, such as fault analysis, protection, and diagnostics in the future.",

author = "Kwon, {Gu Young} and Bang, {Su Sik} and Lee, {Yeong Ho} and Lee, {Geon Seok} and Shin, {Yong June}",

note = "Funding Information: Manuscript received October 29, 2018; accepted February 5, 2019. Date of publication February 13, 2019; date of current version March 13, 2019. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning, #NRF-2017R1A2A1A05001022. This work was also supported under the framework of international cooperation program managed by the National Research Foundation of Korea (2016K2A9A1A03905116). (Corresponding author: Yong-June Shin.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:,yongjune@yonsei.ac.kr). Publisher Copyright: {\textcopyright} 2002-2011 IEEE.",

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N1 - Funding Information: Manuscript received October 29, 2018; accepted February 5, 2019. Date of publication February 13, 2019; date of current version March 13, 2019. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning, #NRF-2017R1A2A1A05001022. This work was also supported under the framework of international cooperation program managed by the National Research Foundation of Korea (2016K2A9A1A03905116). (Corresponding author: Yong-June Shin.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:,yongjune@yonsei.ac.kr). Publisher Copyright: © 2002-2011 IEEE.

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Modeling of high-temperature superconducting cable via time domain reflectometry and general regression neural network

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