http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
까치에 의하여 發生하는 配電線路의 故障豫防에 관한 硏究
전시식,장석구,심재명 한밭대학교 2004 한밭대학교 논문집 Vol.21 No.-
In modern times, electricity is an essential energy and its dependence to people is more increased. The distribution line system in Korea consists of 22.9kV multi-ground connection. The 15.2% of distribution line troubles is being produced by birds' contact with distribution line. The cause of the interruption of electric power is mostly a magpie. To prevent the troubles by magpie, various kinds of prevention measures have been tried for 20 years. But the trouble continues to be happened by magpie's outstanding ability of learning. In this paper, the effectiveness of prevention equipments using a sense of touch, sight and hearing which have been used at Korean Electric Power Corporation is analyzed and more effective prevention measures is considered. As a results, to prevent the troubles, it is difficult to get good effectiveness when only one prevention measure is applied. However, when several measures are applied at the same time, the troubles can be reduced. Also, even though magpies build their nests, for not to make the troubles, a electric pole equipment should be corrected and its form should be improved. In addition, we have found the facts that the activity periods for moderating the number of magpie and for removing the nests, weather, and DB system are simultaneously considered as the effective prevention measures.
전시식(Si-Shik Jeon),차동욱(Dong-wook Cha),김영달(Young-Dal Kim) 대한전기학회 2022 전기학회논문지 Vol.71 No.1
Partial discharge diagnosis is being carried out to prevent failure of ground switch of distribution facilities. However, the reliability of partial discharge diagnosis is insufficient because the results are different depending on how the experts classify the partial discharge and noise signals. Therefore, in this paper, a method to automatically classify partial discharge and noise signals was studied in order to improve the reliability of the partial discharge diagnosis results of ground switch. Partial discharge and noise signal are obtained through the High Frequency Current Transformer(HFCT). And the features were extracted based on the pulse shape analysis, the standard deviation and gravity center of signal. The features are used as input to neural networks and learned using back-propagation. In addition, the structure of neural networks was optimized through genetic algorithm.
배전 가공전선 점퍼용 스리브 최적 압축강도 결정에 관한 연구
전시식(Si-Shik Jeon),박철배(Chul-Bae Park),김영달(Young-Dal Kim) 대한전기학회 2021 전기학회논문지 Vol.70 No.1
This is a study on the determination of optimal compression strength of jumper sleeves for distribution overhead wires using aluminum conductor. In order to determine the optimal compression strength, problems of the existing oiled compression standards were reviewed, and conductor resistance, porosity, tensile strength tests and statistical analysis were conducted to propose new standards. As a result of the test, it was found that 10[tonf], which is less than the current 13[tonf] compressive strength applied to aluminum conductors, is the optimal compression strength. Therefore, in this paper, We propose to reduce the standard for applying the compressive strength of jumpers from 13[tonf] to 10[tonf] or more, which is a place where tension does not occur like jumper sleeve.
전시식(Jeon Si-Shik),김훈(Kim Hun),강찬호(Kang Chan-Ho),양정권(Yang Jung-kwon),장영삼(Jang Young-Sam),이병성(Lee Byoung-Sung),문상근(Moon Snag-Keun),김영달(Kim Young-Dal) 대한전기학회 2021 대한전기학회 학술대회 논문집 Vol.2021 No.10
배전 가공선로 유지보수 점검 및 순시에 적용 가능한 영상인식 기술을 적용한 추적순시장치에 대하여 설명한다. 특히, 전력설비를 대상으로 하는 고속 자동추적 촬영방법, 배전설비의 고속인식과 설비자산의 식별방법을 적용한 자동화된 영상취득 솔루션의 응용으로 인력기반의 순시, 점검 등의 설비운영 분야에 AI기술을 활용한 자율처리 가능성을 검증하였다. 본 연구에서는 차량을 통한 전력설비 순시실증 사례를 통해 주행중 배전설비 고속인식기술과 영상기반 전력설비 관리기술 적용에 대한 내용을 설명한다.
김영달,전시식,차동욱 대한전기학회 2022 전기학회논문지 Vol.71 No.1
Partial discharge diagnosis is being carried out to prevent failure of ground switch of distribution facilities. However, the reliability of partial discharge diagnosis is insufficient because the results are different depending on how the experts classify the partial discharge and noise signals. Therefore, in this paper, a method to automatically classify partial discharge and noise signals was studied in order to improve the reliability of the partial discharge diagnosis results of ground switch. Partial discharge and noise signal are obtained through the High Frequency Current Transformer(HFCT). And the features were extracted based on the pulse shape analysis, the standard deviation and gravity center of signal. The features are used as input to neural networks and learned using back-propagation. In addition, the structure of neural networks was optimized through genetic algorithm.