Time–Frequency-Based Condition Monitoring of 22.9-kV HTS Cable Systems: Cooling Process and Current Imbalance

Lee, Geon Seok; Bang, Su Sik; Kwon, Gu-Young; Lee, Yeong Ho; Sohn, S.H.; Han, Sang-Chul; Shin, Yong-June

doi:10.1109/tie.2018.2881937

Cited by 16 publications

(9 citation statements)

References 35 publications

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“…In addition, the distance between the inlet in the Gumak C/S and the outlet in the HanLim C/S creates a delay for the both pressure and temperature changes. Therefore, in this section, a comparative analysis of performance is conducted for the dependence of the reaction of the newly proposed phase spectrum of CT and the conventional phase difference spectrum of WVD in previous studie [24], [32] to the fluctuations and delay. The performance of the two indices is compared for three time periods.…”

Section: E Results In Real-world System: Chirplet Transform-based Momentioning

confidence: 99%

“…2) The conventional methodologies for anomaly malfunction detection of HTS cables include the phase difference spectrum [24], [33] at the points where the reflected signals are measured using TFDR.…”

Section: B Conventional Time-frequency Domain Reflectometrymentioning

confidence: 99%

“…where k R is the real part of the wave number and x is the location of the reflected signal [24]. Assuming that s 1 is the first measured reflected signal at the impedance change point, and s 2 is the signal measured in real-time at the same location, the TF phase difference spectrum can be used to monitor the variable wave number of the cable system.…”

Section: B Conventional Time-frequency Domain Reflectometrymentioning

confidence: 99%

“…To overcome the challenge of distinguishing and monitoring different joint boxes, time-frequency (TF) analysis and temporal sliding long short-term memory (TS-LSTM) networks are applied. The TF analysis includes the use of Wigner-Ville distribution (WVD) and chirplet transform (CT) which have been previously applied in highresolution spectrum analysis [24]- [26]. In this study, WVD is used to measure the information contents of the reflected signals, and CT is used to monitor the reflected signals that depend on the conditions of the HTS cable systems.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional monitoring techniques using WVD have analyzed reflected signals by assuming that multiple reflections do not exist [24], [32], [33]. However, if, for example, the location of the second joint and the multiple reflection location of the primary joint are similar, it is difficult to identify which of the joints is the target of the analysis.…”

Section: Introductionmentioning

confidence: 99%

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Condition Monitoring of 154 kV HTS Cable Systems via Temporal Sliding LSTM Networks

et al. 2020

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High-temperature superconducting (HTS) cables are expected to be installed in cable tunnels that are already constructed in urban districts. Therefore, the installation of normal joint boxes is inevitable, and it is necessary to develop a diagnostic methodology that considers both the existence of the joints and the electrical characteristics of HTS cables. In this work, temporal sliding long short-term memory (TS-LSTM) is proposed to estimate the locations of the joints that can be hidden by multiple reflections. TS-LSTM includes short-term TS-LSTM and long-term TS-LSTM for analyzing various time dependencies. The reflected signals of the actual joints, which are distinguished from multiple reflections, are analyzed via the chirplet transform (CT) which is one of the time-frequency (TF) analysis methods. The proposed condition monitoring method is applied to an AC 154 kV 600 MVA HTS cable system (1 km) connected to a real power grid network in Jeju, South Korea. For the validation of the proposed methodology, the dielectric and electrical characteristics of the 154 kV HTS cable system are monitored during the cooling process. INDEX TERMS Condition monitoring, long short-term memory (LSTM), superconducting cable, temporal sliding LSTM (TS-LSTM) networks, time-frequency analysis.

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Section: E Results In Real-world System: Chirplet Transform-based Momentioning

confidence: 99%

Section: B Conventional Time-frequency Domain Reflectometrymentioning

confidence: 99%

Section: B Conventional Time-frequency Domain Reflectometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Condition Monitoring of 154 kV HTS Cable Systems via Temporal Sliding LSTM Networks

et al. 2020

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Spatial Resolution between Two Abnormalities in a Cable by Frequency Domain Reflectometry

Ohki

Hirai

2021

IEEJ Transactions Elec Engng

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The spatial resolution of a cable fault location method by frequency domain reflectometry was examined experimentally by giving two abnormalities intentionally along a 31.5‐m coaxial cable. As a result, two clear signal peaks appear, which can locate the presence of two hot spots perfectly if they are separated by more than 30 cm at a position about 15 m from the cable end with a measuring device. In addition, a clear signal peak with a small shoulder is recognized if the two hot spots are 1 cm away. Although the abnormalities were given in this research by heating the cable using tape heaters, the spatial resolution should be theoretically similar for any type of fault in a cable as long as it changes the characteristic impedance of the cable. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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AI-enhanced time–frequency domain reflectometry for robust series arc fault detection in DC grids

Park,

Kwon,

Lee

et al. 2024

Measurement

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Time–Frequency-Based Condition Monitoring of 22.9-kV HTS Cable Systems: Cooling Process and Current Imbalance

Cited by 16 publications

References 35 publications

Condition Monitoring of 154 kV HTS Cable Systems via Temporal Sliding LSTM Networks

Condition Monitoring of 154 kV HTS Cable Systems via Temporal Sliding LSTM Networks

Spatial Resolution between Two Abnormalities in a Cable by Frequency Domain Reflectometry

AI-enhanced time–frequency domain reflectometry for robust series arc fault detection in DC grids

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