This paper describes the electrical characteristics of insulating oil employed in pole transformers. In the experiment, temperature and frequency were selected as variables, and dielectric characteristic values such as the relative permittivity, the tan6 and the specific resistance of the insulating oil were measured by a WinDETA system. As a result, the dielectric characteristics of the testing objects were temperature and frequency-dependent, and especially, the relative permittivities of each object showed considerable difference. In addition, the specific resistance acted as a key factor determining the insulating performance, and it was rather frequency dependent than temperature dependent. And, the dielectric values measured before ageing was slightly different from those after ageing. Dielectric dispersion due to the space charge polarization was observed in all testing objects.
Portable ultrasonic detectors are useful for detection of electrical discharge phenomena like as partial discharge, corona, arching and tracking occurring in an electric system. But the general potable ultrasonic detectors have drawbacks that the results are under the influence subjective reaction of users and it is difficult to determine the type of problem by listening to sound properties and estimate results. So a new analysis method distinguishing ultrasonic characteristics is required. This paper presented ultrasonic wave analysis program to visualize ultrasonic sound of corona, arching and tracking measured by ultrasonic detector. While depending on sound properties alone can be subjective, by incorporating analytical method using the developed program, users are able to increase the accuracy of their ultrasonic diagnosis results.
At present, individual households within a multi-family residential are at susceptible to electrical accidents. As if in a blind spot, they cannot be covered under electrical safety management. This paper presents issues about an autonomous electrical safety management service which uses smart outlets to ensure electrical safety for individual households. Risk factors surrounding electrical incidents occurring in a mashup tech-based environment were analyzed and a smart outlet equipped with a prediction technique was developed. In addition to this, an autonomous electrical safety management service scenario was developed to handle mechanical failures in autonomous electrical safety devices which run on IoT-based smart outlets. By utilizing such an autonomous electrical safety management service model, technology for electrical safety security was developed for individual households or each residential unit in multi-dwelling unit. The correlation among voltage, current, and zero phase current was analyzed to identify the risk factors of electrical accidents and to predict accidents. In order to detect arc faults, a new prediction technique was developed. The technique is based on the mashup technology after analyzing waveform and FFT derived from the correlation among voltage, current, and zero phase current. In particular,
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