DocumentCode :
1800588
Title :
Prediction of Availability for new power plant in the absence of data
Author :
Micali, V.
fYear :
2012
fDate :
15-16 Aug. 2012
Firstpage :
1
Lastpage :
8
Abstract :
This paper is geared in presenting a model to predict the Availability of the Generating Units (GU´s) of New large Coal Fired Stations based on an expert opinion elicitation process, when the data for the Availability is not directly obtainable. The Availability used here is the Energy Availability Factor (EAF, as a percentage) as defined internationally by UNIPEDE (International Union of Producers and Distributors of Electrical Energy) [1]. The EAF is an inferred indicator, i.e. it is derived from the PCLF which is the Planned Capability Loss Factor (maintenance in %), the UCLF (Unplanned Capability Loss Factor) and the OCLF (Other Capability Loss Factor) which are the random percentage losses, the latter being out of management control. After a preliminary statistical analysis, the indications are that the UCLF is at least 300% larger than the OCLF. Therefore, it stands to reason to utilise the UCLF as a determinant for the EAF, since management can adjust the PCLF at will. The results provided are fictitious and for illustrative purpose only.
Keywords :
statistical analysis; steam power stations; EAF; International Union of Producers and Distributors of Electrical Energy; OCLF; PCLF; UCLF; UNIPEDE; coal fired stations; energy availability factor; generating units; opinion elicitation process; other capability loss factor; planned capability loss factor; power plant; preliminary statistical analysis; unplanned capability loss factor; Availability; Bayesian methods; Data models; Distribution functions; Maintenance engineering; Power generation; Predictive models; Dirichlet Process; EAF; Experts in Uncertainty; Non-Parametric Bayes; PCLF; UCLF; Weibull;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial and Commercial Use of Energy Conference (ICUE), 2012 Proceedings of the 9th
Conference_Location :
Stellenbosch
ISSN :
2166-0581
Print_ISBN :
978-1-4673-1241-7
Type :
conf
Filename :
6330169
Link To Document :
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