The power system need to sustain high reliability due to its complexity and security. The reliability prediction method is usually based on independent failure. However, the common cause failure affects many components simultaneously in a system, and it turns out the system collapse seriously in a wide range. Therefore, to improve the reliability of the power system practically, the analysis using the common cause failure is required. This paper describes the common cause failure modeling combined with independent failure. Using the dynamic fault tree, the incorporated independent failure and common cause failure are proposed and analyzed, and it is applied to the power substation in order to examine the method.
Traditional maintenance planning is based on a constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is desirable to be scheduled by RCM(Reliability-Centered Maintenance) evaluation. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system and avoiding or removing maintenance actions that are not strictly necessary. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by the stepwise Markov model in detail. Also, because the system is not continuously monitored, the inspection is considered. In case study, simulation results about RCM will be shown using the real historical data of combustion turbine generating unit in Korean power systems.
Failure Mode Effects and Criticality Analysis (FMECA) is one of most widely used methods in modern engineering system to investigate potential failure modes and its severity upon the system. FMECA evaluates criticality and severity of each failure mode and visualize the risk level matrix putting those indices to column and row variable respectively. Generally, those indices are determined subjectively by experts and operators. However, this process has no choice but to include uncertainty. In this paper, a method for eliciting expert opinions considering its uncertainty is proposed to evaluate the criticality and severity. In addition, a fuzzy expert system is constructed in order to determine the crisp value of risk level for each failure mode. Finally, an illustrative example system is analyzed in the case study. The results are worth considering in deciding the proper policies for each component of the system.
본 논문은 신뢰도 기반 유지보수(RCM : Reliability-Centered Maintenance)계획에 의한 발전설비의 유지보수주기를 평가하고 있다. 유지보수주기는 발전설비의 안정성 및 비용을 결정하기 위한 기준으로 활용된다. 본 논문에서는 설비 수명평가를 통해 확률분포를 이용한 확률론적 TMECA(Failure Mode, Effects and Criticality Analysis)에 대해 논하고, 국내 발전설비중 비교적 평균수명이 짧은 복합화력 발전소를 선정하여 유지보수 주기를 평가하였다. 발전소 유지보수 평가는 향후 구조 개편된 전력시장에서 유지보수계획 수립 방법의 유용한 지표로 활용 가능할 것이다.
The RPN provides information which includes the risk level and the priority order of maintenance tasks for components. However, if there is no sufficient historical failure data, the historical failure data from other sources can be applied to the target system. And if we use historical data from other sources without any process, there will be concomitant problems according to a discord of each system characteristic, a difference between the present and the date of failure data, etc. In this paper, a new methodology is proposed to model the failure rate as a fuzzy function to resolve these problems. Taking advantage of this result, the RPN can be calculated by using the fuzzy operation. The proposed method is applied to the substation system.
FMECA는 전력설비의 기능, 고장 모드, 고장 원인 및 고장의 파급 효과 등을 분석하고 각 고장 모드가 시스템의 기능 유지에 영향을 미치는 정도인 심각도(Severity)와 고장 발생의 빈도의 정도인 치명도(Criticality)를 평가하여 치명도 매트릭스(Criticality Matrix)를 구성함으로써 높은 위험성을 갖는 고장 모드를 판별하고 효과적인 시스템 구성을 위한 참고 자료를 제공한다[4-5]. 대부분의 경우, 고장 모드의 두 지수는 미리 정해진 기준에 따라 전문가들의 정성적인 평가에 의해 결정된다. 따라서 본 논문에서는 두 지수들에 대한 다양한 전문가의 의견을 종합하여 결론을 도출하기 위한 방법론으로 기존의 Linear Opinion Pool에 퍼지이론을 결합하는 방식을 제안하였다. 또한 기존의 치명도 매트릭스 방식으로 위험도를 판별하던 방식의 한계를 인식하고 운영자의 관심에 따라 두 지수를 종합적으로 평가하기 위해 퍼지 FMECA 전문가 시스템을 구성하였다[7-81]. 사례연구에서는 대표적인 전력 설비에 대한 적용 예를 나타내었다.
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