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A Study for the Improvement of the Fault Decision Capability of FRTU using Discrete Wavelet Transform and Neural Network  

Hong, Dae-Seung (남서울대학 전자공학과)
Ko, Yoon-Seok (남서울대학 전자공학과)
Kang, Tae-Ku (한전KDN㈜ 배전IT그룹)
Park, Hak-Yeol (한전KDN㈜ 배전IT그룹)
Yim, Hwa-Young (광운대학교 제어계측공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.56, no.7, 2007 , pp. 1183-1190 More about this Journal
Abstract
This paper proposes the improved fault decision algorithm using DWT(Discrete Wavelet Transform) and ANNs for the FRTU(Feeder Remote Terminal Unit) on the feeder in the power distribution system. Generally, the FRTU has the fault decision scheme detecting the phase fault, the ground fault. Especially FRTU has the function for 2000ms. This function doesn't operate FI(Fault Indicator) for the Inrush current generated in switching time. But it has a defect making it impossible for the FI to be operated from the real fault current in inrush restraint time. In such a case, we can not find the fault zone from FI information. Accordingly, the improved fault recognition algorithm is needed to solve this problem. The DWT analysis gives the frequency and time-scale information. The neural network system as a fault recognition was trained to distinguish the inrush current from the fault status by a gradient descent method. In this paper, fault recognition algorithm is improved by using voltage monitoring system, DWT and neural network. All of the data were measured in actual 22.9kV power distribution system.
Keywords
FRTU(Feeder Remote Terminal Unit); Discrete Wavelet Transform; Neural network; Fault Indicator; Inrush current;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By SCOPUS : 0
연도 인용수 순위
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