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http://dx.doi.org/10.5391/JKIIS.2007.17.3.374

A Study on the Improvement of Fault Detection Capability for Fault Indicator using Fuzzy Clustering and Neural Network  

Hong, Dae-Seung (광운대학교 제어계측공학과)
Yim, Hwa-Young (광운대학교 제어계측공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.3, 2007 , pp. 374-379 More about this Journal
Abstract
This paper focuses on the improvement of fault detection algorithm in FRTU(feeder remote terminal unit) on the feeder of distribution power system. FRTU is applied to fault detection schemes for phase fault and ground fault. Especially, cold load pickup and inrush restraint functions distinguish the fault current from the normal load current. FRTU shows FI(Fault Indicator) when the fault current is over pickup value or inrush current. STFT(Short Time Fourier Transform) analysis provides the frequency and time Information. FCM(Fuzzy C-Mean clustering) algorithm extracts characteristics of harmonics. The neural network system as a fault detector was trained to distinguish the inruih current from the fault status by a gradient descent method. In this paper, fault detection is improved by using FCM and neural network. The result data were measured in actual 22.9kV distribution power system.
Keywords
Fuzzy Clustering; Power distribution system; FI; Neural Network; FRTU;
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