• KIEE International Transactions on Electrophysics and Applications
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• v.3C no.3
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• pp.91-98
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• 2003

The importance of improving the quality of electric power is being strongly raised, owing to an increasing use of sensitive and small-sized electronic devices and systems. The transient over-voltages on low-voltage power distribution systems are induced by direct or indirect lightning return strokes. These can cause damage and/or malfunction of the utility systems for home automation, office automation, factory automation, medical automation, etc. The behaviors of lightning overvoltages transferred through the transformer to the low-voltage distribution systems using a Marx generator were experimentally investigated. Furthermore, the coupling mechanisms of lightning overvoltages transferred to the low-voltage systems were clearly illustrated through a theoretical simulation using a Pspice program. The overvoltages in low-voltage ac power systems are rarely limited by the application of the surge arrester to the primary side of the distribution transformer. A superior surge protection scheme is to install surge protection devices at the service entrance switchboard and/or at the load devices in TN power systems.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.55-65
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• 2019

With the rapid development of the national industry, the importance of electrical safety was recognized because of a lot of new electrical equipment are installing and the electrical accidents have been occurring annually. Today, the electrical equipments is inspect by using the portable Infrared thermal imaging camera. but the most negative element of using the camera is inspected for only state of heating, the reliable diagnosis is depended with inspector's knowledge, and real-time monitoring is impossible. This paper present the infrared thermal imaging safety diagnosis system. This system is able to monitor in real time, predict the state of fault, and diagnose the state with analysis of thermal and power data. The system consists of a main processor, an infrared camera module, the power data acquisition board, and a server. The diagnostic algorithm is based on a mathematical model designed by analyzing the Pearson's Correlation Coefficient between temperature and power data. To test the prediction algorithm, the simulations were performed by damaging the terminals or cables on the switchboard to generate a large amount of heat. Utilizing these simulations, the developed prediction algorithm was verified.