• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.579-583
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• 1988

As the power facilities grow up, the optimal operation and the best maintenance of power plant can not be overestimated too much, which can enhance the plant availability and reliability much further. In this respect, fault diagnosis methodologies of dynamic system which is time-varing and strongly nonlinear have been studied. On of them is to use algorithm which is based on time-invariant, linear system, but this is not so nice a method for applying to power Plant. Therefore, the study on other techniques using Artificial Intelligence (AI) is under way. In this paper, the existing ways of fault detection are surveyed and their problems are also discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.594-597
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• 2001

As many industrial systems become more complex, they become extremely difficult to diagnose the cause of failures. The subject of this paper is ECCS(Emergency Core Cooling System) part of PWR(Pressurized Water Reactor). This paper presents modeling and diagnoser construction of ECCS based on discrete event system theory. Also, this paper presents that the ECCS system is diagnosible in our approach.

• Tribology and Lubricants
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• v.10 no.4
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• pp.51-58
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• 1994

Condition monitoring technology has recently been received much attention in the light of its significance on the maintenance of complex machineries such as turbines in power plants. Currently, turbines in power plants are maintained by scheduled overhaul based on the manufacturer's recommendations and the utility's experience. Although this preventive maintenance is known to be very effective, operators have less access to identify failure of elements when it happens between overhaul period. Therefore, in this study, a development of a on-line condition monitoring system through wear debris analysis of lubricating oils is aimed with a view to detecting abnormal wear behaviour of bearings and other wet-components at an early stage, allowing better outage scheduling and minimizing forced outages. For field application purposes, the on-line system developed was installed on the turbine of the No.4 unit at Ulsan Power Plant and its performance has been evaluated on site.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.185-186
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• 2015

TSC는 전력계통의 무효전력 보상을 위한 SVC 시스템의 주요 장치이다. 본 논문에서는 TSC의 커패시터 뱅크의 열화 및 고장진단에 활용할 수 있는 커패시터 용량 측정 기법을 제안한다. 제안한 커패시터 용량 측정 기법은 시간영역 해석 기법으로서 커패시터의 전압 및 전류 신호에 포함된 고조파 및 잡음, 주파수 변동, 과도 상태 전이 등의 왜란에 강인하다.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.107-109
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• 1999

In the point of view protective relay, the selectivity and sensitivity is very important in its performance. The correct setting of the relay parameter is required. The verification of accurate setting for the protective relay is very difficult before a actual fault occurs. In this paper, we proposed the diagnosis expert system as a method to verify the correctness of the relay setting. The developed system proved effectiveness through the tests on the real systems.

• Bulletin of the Korea Photovoltaic Society
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• v.1 no.1
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• pp.42-48
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• 2015

태양광발전시스템 기술은 태양광모듈-접속함-PCS가 포함되는 BOS 기술로부터 시작되어 독립형 또는 계통연계형으로 전력을 생산하게 하는 설치, 운전, 운영기술이 모두 포함된다. 태양광 밸류체인 기술마다 급속한 산업 성장으로 단가경쟁력이 치열한 요즘 태양광발전시스템 기술은 설치단가, 발전단가를 낮출 수 있는 최후의 마지막 수단이라고 생각하고 시스템 설치단가, 운영단가를 낮추기 위해 많은 연구를 수행 중에 있다. 그러나, 태양광발전시스템 기술이 정상적으로 완성되지 못하면 경제적 손실은 물론, 내구성능과 수명, 민원에 의한 분쟁, 사고 등 다양한 고통을 겪게 된다. 본고에서는 이러한 태양광발전시스템에 대한 다양한 고장 사례와 민원 분쟁 유형을 진단하고, 그에 대한 해결방안을 제시하고자 한다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.863-871
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• 1999

While Logic Based Expert System (LBES) has a merit of rapid and complete inference, it also has a defect of huge knowledge base. Hierarchical LBES (HLBES) replaces the assertion time inference of LBES with the multi-level logic minimization procedure, and it guarantees smaller knowledge base comparing with LBES. This paper has two contributions. The one is proposing so-called fact-minimization procedure which reduces not only the number of facts or measured events but also the size of knowledge base dramatically. The other contribution is application of HLBES and the proposed fact-minimization to the fault diagnosis of power system. The application is successfully performed in the example with the transmission system which takes 72 goals and 352 facts.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.2
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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.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.278-281
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• 2001

Identifying the fault location has to deal with multiple possible candidates. It presents a new technique to diagnosis a fault region. The proposed diagnosis scheme identifies accurately the location based on the protective device operations and load current change at the substation. An Expert system is presented using these facts.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.819-821
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• 1998

This paper deals with a method or a residual generation for fault isolation in a centrifugal pump with a water circulation system, driven by a speed controlled dc motor. It is based on parity relations derived from the moving-average model of the system and is used to identify sensor faults and two possible brush and impeller faults, where the former is dealt with additive faults, while the latter characterized as discrepancies between the nominal and actual plant parameters of the system is modelled by multiplicative faults. We will represent the propagation of this uncertainty to the model matrices by the approximate handling of partial derivatives of polynomials. With multiplicative faults, the transformation matrix implemented in the residual generator are calculated on-line. The simulation studies demonstrate that small changes of the system can be detected and diagnosed by using the method.