Global Sensitivity Analysis of Multi-State Markov Reliability Models of Power Equipment Approximated by Polynomial Chaos Expansion

Abstract : Reliability and availability of electric power system equipment (e.g., generator units, transformers) are often evaluated by defining and solving Markov models. Transition rates among the identified equipment states are estimated from experimental and field data, or expert judgment, with inevitable uncertainty. For model understanding and to guide validation and confidence building, it is of interest to investigate the effects of the uncertainty in the input transition rates on the output reliability and availability. To this aim, Global Sensitivity Analysis (GSA) can be used for defining importance (sensitivity) indexes that allow a ranking of the transition rates with respect to their influence on the uncertainty in the output. In general, GSA requires a large number of model evaluations. In this paper, a metamodel is defined to estimate the performance index of interest (e.g. reliability or availability). The metamodel is built based on polynomial chaos expansion (PCE), a multidimensional polynomial model approximation whose coefficients are determined by evaluating the model in a reduced set of predetermined values of the input. The proposed approach is illustrated on a power generating unit.
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Submitted on : Friday, January 24, 2014 - 5:39:56 PM
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Claudio Rocco, Enrico Zio. Global Sensitivity Analysis of Multi-State Markov Reliability Models of Power Equipment Approximated by Polynomial Chaos Expansion. Journal of KONBiN, 2013, 23 (1), pp.59-70. ⟨10.2478/jok-2013-0038⟩. ⟨hal-00936273⟩

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