Energy Informatics LAB. (에너지정보처리 연구실)

As dependence on power cables is increasing correspondingly with skyrocketing demand for electric power every year, securing the reliability of power cables is important. In this research, the reliability of the power system is improved through the development of cable diagnosis and monitoring techniques based on time-frequency domain reflectometry. This research includes extending cable diagnostics to intelligent information technology (diagnosis automation, fault classification) by means of artificial intelligence. 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. 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