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불충분한 고장 데이터에 기초한 발전소의 신뢰도 산정기법에 관한 연구

Authors
이승철최동수
Issue Date
May-2003
Publisher
대한전기학회
Keywords
Power Plant Reliability; Weibull Reliability Analysis; Median Rank; Multinomial Distribution; Availability; Power Plant Reliability; Weibull Reliability Analysis; Median Rank; Multinomial Distribution; Availability
Citation
전기학회논문지 A권, v.52, no.7, pp 401 - 406
Pages
6
Journal Title
전기학회논문지 A권
Volume
52
Number
7
Start Page
401
End Page
406
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65666
ISSN
1229-2443
Abstract
- Electric power industries in several countries are currently undergoing major changes, mainly represented by the privatizations of the power plants and distribution systems. Reliable operations of the power plants directly contribute to the revenue increases of the generation companies in such competitive environments. Strategic optimizations should be performed between the levels of the reliabilities to be maintained and the various preventive maintenance costs, which require the accurate estimations of the power plant reliabilities. However, accurate estimations of the power plant reliabilities are often limited by the lack of accurate power plant failure data. A power plant is not supposed to be failed that often. And if it fails, its impact upon the power system stability is quite substantial in most cases, setting aside the significant revenue losses and lowered company images. Reliability assessment is also important for Independent System Operators(ISO) or Market Operators to properly assess the level of needed compensations for the installed capacity based on the availability of the generation plants.In this paper, we present a power plant reliability estimation technique that can be applied when the failure data is insufficient. Median rank and Weibull distribution are used to accommodate such insufficiency. The Median rank is utilized to derive the cumulative failure probability for each ordered failure. The Weibull distribution is used because of its flexibility of accommodating several different distribution types based on the shape parameter values. The proposed method is applied to small size failure data and its application potential is demonstrated.
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