• 한국지능시스템학회논문지
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• 제12권6호
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• pp.503-510
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• 2002

본 논문에서는 비선형시스템에서 발생한 고장을 감지하고 분류하기 위해 신경회로망기반 다중고장모델과 통계적기법에 의한 고장진단 방법을 제안한다. 제안한 알고리듬에서는 시스템의 출력과 신경회로망 공칭모델 출력 사이의 오차가 미리 설정한 문턱 값을 넘으면 고장을 감지한다. 고장이 감지되면 고장분류기에서는 각 신경회로망 고장모델 출력과 시스템 출력 사이의 오차를 이용하여 통계적 기법으로 고장을 분류한다. 컴퓨터 시뮬레이션 결과로부터 제안한 고장진단방법이 비선형 시스템에서의 고장감지 및 분류문제에 잘 적용됨을 알 수 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.173-180
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• 2016

Fault detection and diagnosis is a task to monitor the occurrence of faults and pinpoint the exact location of faults in the system. Fault detection and diagnosis is gaining importance in development of efficient, advanced and safe industrial systems. Three phase inverter is one of the most common and excessively used power electronic system in industries. A fault diagnosis system is essential for safe and efficient usage of these inverters. This paper presents a fault detection technique and fault classification algorithm. A new feature extraction approach is proposed by using three-phase load current in three-dimensional space and neural network is used to diagnose the fault. Neural network is responsible of pinpointing the fault location. Proposed method and experiment results are presented in detail.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1727-1731
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• 2004

We propose an ART2 neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, fault classifier by ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters is used for fault isolation. The performances of the proposed fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.127.1-127
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• 2001

In this paper we propose an input/output model based fault diagnosis method to detect and isolate single faults in the robot arm control system. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation, When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, and in this zone the estimated parameters are transferred to the fault classifier by ART2(adaptive resonance theory 2) neural network for fault isolation. Since ART2 neural network is an unsupervised neural network fault classifier does not require the knowledge of all possible faults to isolate the faults occurred in the system. Simulations are carried out to evaluate the performance of the proposed ...

• 대한전기학회논문지:전력기술부문A
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• 제50권2호
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• pp.53-57
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• 2001

This paper presents the new methods of fault diagnosis through multiple alarm processing of protective relays and circuit breakers in power systems using probabilistic neural networks. In this paper, fault section detection neural network (FSDNN) for fault diagnosis is designed using the alarm information of relays or circuit breakers. In contrast to conventional methods, the proposed FSDNN determines the fault section directly and fast. To show the possibility of the proposed method, it is simulated through simulation panel for Sinyangsan substation system in KEPCO (Korea Electric Power Corporation) and the case studies show the effectiveness of the probabilistic neural network mehtod for the fault diagnosis.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1789-1794
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• 2014

This paper suggests an application method for a superconducting fault current limiter (SFCL) using an evaluation index to estimate the risk regarding the short-circuit capacity of the circuit breaker (CB). Recently, power distribution systems have become more complex to ensure that supply continuously keeps pace with the growth of demand. However, the mesh or loop network power systems suffer from a problem in which the fault current exceeds the short-circuit capacity of the CBs when a fault occurs. Most case studies on the application of the SFCL have focused on its development and performance in limiting fault current. In this study, an analysis of the application method of an SFCL considering the risk of the CB's short-circuit capacitor was carried out in situations when a fault occurs in a loop network power system, where each line connected with the fault point carries a different current that is above or below the short-circuit capacitor of the CB. A loop network power system using PSCAD/EMTDC was modeled to investigate the risk ratio of the CB and the effect of the SFCL on the reduction of fault current through various case studies. Through the risk evaluations of the simulation results, the estimation of the risk ratio is adequate to apply the SFCL and demonstrate the fault current limiting effect.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.599-605
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• 2018

Fault diagnosis depends critically on the selection of sensors monitoring crucial process variables. Boolean network (BN) is composed of nodes and directed edges, where the node state is quantized to the Boolean values of True or False and is determined by the logical functions of the network parameters and the states of other nodes with edges directed to this node. Since BN can describe the fault propagation in a sensor network, it can be applied to propose sensor selection strategy for fault diagnosis. In this article, a sufficient condition for parameter identifiability of BN is first proposed, based on which the sufficient condition for fault identifiability of a sensor network is given. Then, the fault identifiability condition induces a sensor selection strategy for sensor selection. Finally, the theoretical result is applied to the fault diagnosis-oriented sensor selection for a nuclear heating reactor plant, and both the numerical computation and simulation results verify the feasibility of the newly built BN-based sensor selection strategy.

• 설비공학논문집
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• 제15권3호
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• pp.159-165
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. To detect partial faults of the air-conditioning system, a neural network algorithm may be used. In this study, the neural network algorithm using normalized input data by the standard deviation was applied. And the [7$\times$10$\times$10$\times$1] neural network structure was selected. Test results showed that the neural network algorithm using normalized input data was very effective to detect the condenser fouling and the evaporator fan fault of an air-conditioning system.

• 전기학회논문지
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• 제60권3호
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• pp.465-470
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• 2011

Recently fault currents are increasing in a network. It is caused by increase in electric demand and high penetration of distributed generation with renewable energy sources. Moreover, distribution network has become more and more complex as mesh network to improve the distribution system reliability and increase the flexibility and agility of network operation. Accordingly, the fault current will exceed capacity of circuit breakers soon and all the various rational solutions to solve this problem are taken into account. Under these circumstances, superconducting fault current limiter(SFCL) is a new alternative in the viewpoint of technical and economic aspects. This study presents operation processes for a resistive-type of SFCL, and it proposes reliability model for the SFCL. When a SFCL is installed into a network, the contribution of decreased fault currents to failure for distribution equipments can be quantified. As a result, it is expected that a SFCL makes the reliability of adjacent equipments on existing network improve and these changes are analyzed. We propose a methodology to evaluate the reliability in the distribution network where a SFCL is installed considering a reliability model for resistive-type of SFCL and reliability changes for adjacent equipments which are proposed in this paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.124-127
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• 1993

This paper presents the new algorithm for fault analysis and the fault analysis package for executing this algorithm. This algorithm obtains requisite term for fault analysis by the two-port network technique. Therefore, the fault calculation time is minimized because ${Y_{BUS}}^{-1}$ calculation time is removed. And, the graphic user environment for fault analysis is implemented in mouse-oriented user interface with window and pull-down menu. Therefore, this package can be a useful tool for fault analysis.