In recent years, increasing electricity use has led to considerable interest in green energy. In order to effectively supply, cut off, and operate an electric power system, many electric power facilities such as gas insulation switch (GIS), cable, and large substation facilities with higher densities are being developed to meet demand. However, because of the increased use of aging electric power facilities, safety problems are emerging. Electromagnetic wave and leakage current detection are mainly used as sensing methods to detect live-line partial discharges. Although electromagnetic sensors are excellent at providing an initial diagnosis and very reliable, it is difficult to precisely determine the fault point, while leakage current sensors require a connection to the ground line and are very vulnerable to line noise. The partial discharge characteristic in particular is accompanied by statistical irregularity, and it has been reported that proper statistical processing of data is very important. Therefore, in this paper, we present the results of analyzing Φ-q-n cluster distributions of partial discharge characteristics by using K-means clustering to develop an expert partial discharge diagnosis system generated in a GIS facility.

The amount of electrical energy has been increased with the rapid development of the industrial society. Accordingly, operating voltage of the power equipment and facility capacity are continuously increasing. Development trends of recent high-voltage electrical equipment are ultra high-voltage, large-capacity and compact. Early diagnosis of a failure of the power plant has been emerging as an important task as to supply high quality power to users. In this study, we have tried to develope an algorithm for distinguishing an arc fault signal generated in the power plant by using UV sensor.