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Advance first order second moment (AFOSM) method for single reservoir operation reliability analysis: a case study.
- Source :
-
Stochastic Environmental Research & Risk Assessment . Jan2012, Vol. 26 Issue 1, p33-42. 10p. 5 Graphs. - Publication Year :
- 2012
-
Abstract
- Reservoir system reliability is the ability of reservoir to perform its required functions under stated conditions for a specified period of time. In classical method of reservoir system reliability analysis, the operation policy is used in a simple simulation model, considering the historical/synthetic inflow series and a number of physical bounds on a reservoir system. This type of reliability analysis assumes a reservoir system as fully failed or functioning, called binary state assumption. A number of researchers from various research backgrounds have shown that the binary state assumption in the traditional reliability theory is not extensively acceptable. Our approach to tackle the present problem space is to implement the algorithm of advance first order second moment (AFOSM) method. In this new method, the inflow and reservoir storage are considered as uncertain variables. The mean, variance and covariance of uncertain variables are determined using moment values of reservoir state variables. For this purpose, a stochastic optimization model developed based on the constraint state formulation is applied. The proposed model of reliability analysis is used to a real case study in Iran. As a result, monthly probabilities of water allocation were computed from AFOSM method, and the outputs were compared with those from Monte Carlo method. The comparison shows that the outputs from AFOSM method are similar to those from the Monte Carlo method. In term of practical use of this study, the proposed method is appropriate to determine the monthly probability of failure in water allocation without the aid of simulation. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RESERVOIRS
*SIMULATION methods & models
*ALGORITHMS
*MONTE Carlo method
Subjects
Details
- Language :
- English
- ISSN :
- 14363240
- Volume :
- 26
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Stochastic Environmental Research & Risk Assessment
- Publication Type :
- Academic Journal
- Accession number :
- 67684467
- Full Text :
- https://doi.org/10.1007/s00477-011-0517-1