* C.-K. Lee, and Y.-J. Shin, “Detection and Assessment of I&C Cable Faults Using Time-Frequency R-CNN Based Reflectometry,” IEEE trans. Ind. Electron., vol. 68, no. 2, pp. 1581-1590, Feb. 2020.
Condition-Based Cable Health Index
An equipment having poor reliability can cause deadly failures and have corresponding adverse effects on the entire power system. Therefore, the condition of the equipment should be checked and maintained to determine marginal replacement time.
The goal of this research is to prevent cable failures and accidents in advance by indexing the condition of cables based on operational data such as cable load and failure histories.
The influences of thermal and mechanical stress on the lifespan of cables are investigated to propose a health index that reflects the overall complex stress, which will aid in securing the reliability of the cables.
Development of intelligent health index of cable that reflects operational data in real time.
Investigation on the relationship between thermal and mechanical stress that affects the lifespan of cables and the proposed health index.
▲ Neural network-based cable health index algorithm *
▲ Cable temperature estimation using thermal circuit-based cable modeling **
* G. H. Ji, S. S. Bang, Y. H. Jung, T. I. Jang, and Y.-J. shin, “Ensemble Learning-Based Health Index for Underground Transmission Line,” Submitted to IEEE Trans. Ind. Informat.
** KEPCO KDN Research Project, “Development of Dynamic Rating System and Health Indexing for Distribution Cable.”
Extended Applications of Reflectometry
Diagnostic techniques in various fields have limitations in their performance because the diagnostic techniques analyze signals in the time domain or the frequency domain only.
Therefore, the research aims to improve the performance of the conventional diagnostic techniques through the convergence between the conventional diagnostic techniques and time-frequency domain reflectometry, which shows excellent performance in power cable diagnostics.
By analyzing the signal propagation characteristics of the diagnosis target, the type of diagnosis signal is determined, and research on the signal selection and application method is conducted.
Ongoing research includes ultrasonic-based reflectometry for pipeline diagnosis and laser-based reflectometry for optical cable diagnosis.
Ultrasonic guided wave-based time-frequency domain reflectometry for diagnosis of pipelines
Intensity modulated laser-based time-frequency domain reflectometry for diagnosis of optical cables