Monitoring Electrical and Thermal Characteristics of HTS Cable Systems via Time–Frequency Domain Reflectometry

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

doi:10.1109/tasc.2017.2652330

Cited by 23 publications

(9 citation statements)

References 9 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%

“…TFDR can be used to calculate the amount of signal information as well as detect cable anomalies as follows: 1) TF cross-correlation (TFCC) is conventionally used in TFDR, particularly to resolve multiple elementary signals by measuring the signal information and indicating the separation points [32].…”

Section: B Conventional Time-frequency Domain Reflectometrymentioning

confidence: 99%

“…where T s is the time duration of the incident signal [32]. The TFCC function quantifies the similarity between the incident and reflected signals as a value between 0 and 1, thereby indicating the presence of a reflected signal.…”

Section: B Conventional Time-frequency Domain Reflectometrymentioning

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%

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

et al. 2020

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“…LEHV), Extension Spectrum Time Domain Reflection (SSTDR), etc. [4][5][6][7][8] . At present, the most mature cable fault detection method is TDR, and the best detection effect is SSTDR.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Cable Fault Detection and Location Based on SSTDR

Wang¹,

Xiao²,

Wang³

et al. 2019

dtcse

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In the detection and positioning of aircraft cable faults based on Time Domain Reflectometry (TDR), due to the multiple cable connectors of the aircraft and in a severely noisy environment, the reflected signals are severely attenuated and waveform distortion exists, making it impossible to achieve accurate cable fault detection. In order to solve these problems, this paper designs the spread spectrum time domain reflectometry (SSTDR) for the characteristics of the aircraft cable, selects the m-code as the original signal, outputs the BPSK modulation with a modulation ratio of 0.5, and finally uses the correlation algorithm to obtain the fault type and distance. Using a signal source, an aircraft cable and an oscilloscope, an actual measurement system is used to test aircraft cables of different lengths. The test results show that SSTDR has high noise immunity and detection accuracy in actual tests.

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Abnormal line loss identification system of low voltage distribution network based on HPLC communication technology

Tang¹,

Chang²,

Liang³

et al. 2023

The Journal of Engineering

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Power cable is mainly responsible for the transmission and distribution of electric energy in the power grid. The performance of a power cable greatly affects the reliability and security of the power supply. In order to solve the problem of cable aging caused by longterm operation and various internal and external factors, this paper proposes an abnormal line loss identification method for a low-voltage distribution network station area, aiming at focusing on the monitoring of cable aging. The transmission characteristic model based on the transmission line theory and the dielectric constant model of the aging cable insulation layer is established, and the internal relationship between the transmission characteristic and the aging characteristic is analyzed. The characteristic extraction algorithm is used to extract the characteristics of the transmission characteristic curve. Through simulation analysis, for the abnormal line loss fault, the location accuracy of different fault locations is basically at the level of 0.1-0.3%. The simulation of local defects in the cable shows that the change of material dielectric constant is more clear for the characterization of cable aging. The monitoring method in this paper is helpful to provide the relevant basis for the line maintenance plan and the operation strategy of the power grid and is of great significance to ensure the safe and stable operation of the power grid.

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Monitoring Electrical and Thermal Characteristics of HTS Cable Systems via Time–Frequency Domain Reflectometry

Cited by 23 publications

References 9 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

Aircraft Cable Fault Detection and Location Based on SSTDR

Abnormal line loss identification system of low voltage distribution network based on HPLC communication technology

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