• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2228-2230
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• 2002

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the Lifting and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of lifting scheme to the various HIF data. These data were measured in actual 22.9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1015-1017
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• 1997

A high impedance fault on the multi-grounded three-phase four-wire distribution system can not be detected by conventional overcurrent sensing devices. In this paper, the neural network is used to detect high impedance faults. The proposed algorithm using back - propagation neural network is demonstrated by simulation with the staged fault test data. The harmonic components of current and the phase of voltage are used as the inputs of neural network. Results of the simulation can be used as a reference for the development of a high impedance fault detector.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2457-2464
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• 2013

In this paper, we design and analyze a mutual exclusion algorithm, based on the Token and Failure detector, in asynchronous distributed systems. A Failure Detector is an independent module that detects and reports crashes of other processes. There are some of advantages in rewriting the Token-based ME algorithm using a Failure Detector. We show that the Token-based ME algorithm, when using Failure Detector, is more effectively implemented than the classic Token-based ME algorithm for synchronous distributed systems.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.10
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• pp.553-561
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• 2004

Leader election is an important problem in developing fault-tolerant distributed systems. As a classic solution for leader election, there is Garcia-Molina's Bully Algorithm based on time-outs in synchronous systems. In this paper, we re-write the Bully Algorithm to use a failure detector instead of explicit time-outs. We show that this algorithm is more efficient than the Garcia-Molina's one in terms of the processing time. That is because the Bully_FD uses FD to know whether the process is up or down so fast and it speed up its execution time. Especially, where many processes are connected in the system and crash and recovery of processes are frequent, the Bully_FD algorithm is much more efficient than the classical Bully algorithm in terms of the processing time.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.271-276
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• 2021

Arc fault detector based on multilevel DWT with analysis of high-frequency noise components over 100 kHz is proposed in this study to improve the performance in detecting serial arcs and distinguishing them from inverter noise in PV systems. PV inverters generally operate at a frequency range of 20-50 kHz for switching operation and maximum power tracking control, and the effect of these frequency components on the signal for arc detection leads to negative arc detection. High-speed ADC and multilevel DWT are used in this study to analyze frequency components above 100 kHz. Such high frequency components are less influenced by inverter noise and utilized to detect as well as separate DC series arc from inverter noise. Arc detectors identify the input current of PV inverters using a Rogowski coil. The sensed signal is filtered, amplified, and used in 800kSPS ADC and DWT analysis and arc occurrence determination in DSP. An arc detection simulation facility in UL1699B was constructed and AFD tests the proposed detector were conducted to verify the performance of arc detection and performance of distinction of the negative arc. The satisfactory performance of the arc detector meets the standard of arc detection and extinguishing time of UL1699B with an arc detection time of approximately 0.11 seconds.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11B
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• pp.956-966
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• 2004

Nowadays, NGN(Next Generation Network) is evolving into network with packet based QoS.\ulcorner This paper presents the recovery method as a selecting way of more efficient path in MPLS network that reduces the service interruption time. In order to increase the efficiency of usage of recovering resource which is one of major functions of disorder recovering, it opens the state information of each Node both to fault detector and bandwidth broker so that it reduces message transmission time for recovery when functional disorder occurs. in addition, by effective management and adjustment of required bandwidth of entire network depending on the changes of traffic load, it improves the efficiency of bandwidth usage. and in MPLS network supporting priority selection function, it allocates domains depending on the traffic relating to priority order to the entire network so that\ulcorner it efficiently allocates proper resources to the restoring process between the entry and the end for the traffic of high priority and for low priority traffic, it restores path oかy inside disordered domain. by doing this, it reduces resetting time and unnecessary massage transmission.

• Journal of the Korea Convergence Society
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• v.4 no.3
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• pp.21-26
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• 2013

Currently almost people use a personal computer for various purpose. But some people are not familiar to computer system. If they see only black screen on the monitor when they turn on the computer power, they can not recognize whether it is hardware or software faults. So, in this paper is aimed to develop the module of computer hardware fault detecter using ROM BIOS before OS booting. This module use PCI interface with mother board of computer. Before os booting, it can get the ROM BIOS memory by interrupt and show what hardware is fault according to the predefined memory content of BIOS.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2184-2186
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• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the fast wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of fast wavelet transform to the various HIF data. These data were measured in actual 22.9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.2
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• pp.72-75
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• 2005

In oil-filled equipment such as transformers, partial discharge or local overheating will precede a final shutdown. Accompanied with such problems is a decomposition of insulating material into gases, which are dissolved into the transformer oil. The gases dissolved in oil can be separated with some membranes based on the differences in permeability of membranes to different gases. This paper discuss the permeability characteristics of several membranes for separation hydrogen gas in oil. With result of this paper, it may become possible to detect fault-related gases from transformer oil and predict incipient failures in the