• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1183-1190
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• 2007

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.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1113-1120
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• 2022

In this paper, a communication methodology for the digital FRTU(: Feeder Remote Terminal Unit) required by the smart grid distribution system was studied. The digital FRTU consists of a fault handling unit and a communication unit. The fault handling unit transmits fault information to the communication unit in case of a failure, and the communication unit is designed to autonomously determine the fault section through two-way communication between surrounding digital FRTUs. For performance verification, a performance verification system consisting of 3 line sections based on 3 digital FRTUs was constructed to enable fault simulation for various failure scenarios. Various fault cases including one phase ground fault, line-to-line short-circuit fault, and three-phase short-circuit fault were experimentally simulated on the established performance verification system, and the validity of the developed methodology was confirmed by proving the accurate fault section inference results for each fault simulation case.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.374-379
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• 2007

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.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.46-48
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• 2007

This paper presents research about improvement of fault detection algorithm in FRTU on the feeder of distribution system. FRTU(Feeder Remote Terminal Unit) is applied to fault detection schemes for phase fault, ground fault, and cold load pickup and Inrush restraint functions distinguish the fault current and the normal load current. FRTU is occurred FI(Fault Indicator) when current is over pick-up value also inrush current is occurred FRTU indicate FI. Discrete wavelet transform(DWT) analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate inrush current from the fault status by a gradient descent method. In this paper, fault detection is improved using voltage monitoring system with DWT and neural network. These data were measured in actual 22.9kV distribution system.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.446-448
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• 2001

We have developed the testing software for KEPCO operators to maintain the small-scale distribution automation system effectively. The testing software is used to monitor the present status, to measure the analog value(voltage, amperage) and to control the switch and FRTU(Feeder Remote Terminal Unit) in order to find out a disabled part of the system. And at the stage of inspection of the switch and FRTU, it can screen the inferior one.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.9
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• pp.83-89
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• 2013

Power distribution system has been changed from radial system to closed loop or mesh system due to connection of distributed generation growth. Data from distribution equipments which are installed at distribution line is required to be accurate for the performance of DMS(Distribution Management System). This paper analyzes the voltage measurement data from distribution equipment. However, the results of the analysis are confirmed to have some errors in voltage measurement data from distribution equipment. These errors come from aging of voltage sensor in distribution equipment and inaccurate data transfer to FRTU(feeder remote terminal unit) through the controller. The main problem is that the voltage measurement data of distribution equipment can not be assessed after it's first installation at the distribution line. The voltage measurement accuracy assessment system is to assess the voltage measurement data from distribution equipment on hot-line. This study had a field test to verify the performance of system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.1
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• pp.65-69
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• 2020

The aim of this study is to improve the fault decision ability of FRTU (Feeder remote terminal unit) in DAS (Distribution automation system). FRTU uses the FI (Fault indicator) algorithm based on fault current pickup and operation of the protection device. Even if the inrush current flows or the protection device is sensitive to the transient current, FRTU may indicate incorrect fault information. To address these problems, we propose an improved fault recognition algorithm that can be applied to FRTU. We will detect a specific wave that is indicative of a fault, and use this information to identify a fault wave. The specific wave-detection algorithm is based on the duration and periodicity of the voltage, current, and harmonic variations. In addition, we propose fault recognition algorithms using voltage factor variation analysis and DWT (Discrete wavelet transform). All the wave data used in this study were actual data stored in FRTU.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.497-498
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• 2007

When we implement the substation automation, the largest problem is that IEDs may come from different vendors, which will result in the reduction of the reliability of communication between IEDs. To solve the problem IEC61850 is investigated, which uniforms the communication interface. However, in order to communicate with an IEC61850 implanted IED, FRTU (Feeder Remote Terminal Unit) need to be compatible with IEC6185 consequentially. A new IEC61850 based FRTU development scheme is proposed. The performance shows his compatibility with other IEC61850 IED, which has already been tested in ARET (Agent-based Reliability Enhancement Technology) system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.2
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• pp.151-156
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• 2019

The aim of this investigation is to detect specific waveforms in a distribution line prior to the occurrence of a fault. Conditions were introduced such that a feeder remote terminal unit (FRTU) of the distribution automation system selects and stores fault waveforms from the different waveforms detected in the distribution line. In addition, an algorithm was developed to detect specific waveforms from the fault waveforms stored using the FRTU. This algorithm exploits the duration and periodicity of harmonic changes in voltage and current. The efficacy of the algorithm was confirmed based on the measurements of fault waveforms in an actual distribution line. The results indicated that faults in a distribution line can be predicted via experimental measurements.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.31-32
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• 2007

In this paper, a fault section detection method is proposed for ungrounded system in the case of a single line-to-ground fault. A conventional method is used for faulted feeder selection according to the angular relationship between zero-sequence currents of the feeders and zero-sequence voltage of the system. Fault section detection is based on the comparison of phase angle of zero-sequence current. Proposed method has been testified in a demo system by Matlab/Simulink simulations. Based on Distribution Automation System(DAS), Feeder Remote Terminal Unit(FRTU) is used to collect those necessary data, at present a demo system is under developing using Manufacturing Message Specification (MMS) in IEC61850 standard.