NDLI: Extracting rare failure events in composite system reliability evaluation via subset simulation

Content Provider	IEEE Xplore Digital Library
Author	Bowen Hua Zhaohong Bie Siu-Kui Au Wenyuan Li Xifan Wang
Copyright Year	2015
Description	Author affiliation: Sch. of Electr. Eng., Chongqing Univ., Chongqing, China (Wenyuan Li) \|\| Inst. for Risk & Uncertainty, Univ. of Liverpool, Liverpool, UK (Siu-Kui Au) \|\| Sch. of Electr. Eng., Xi'an Jiaotong Univ., Xi'an, China (Bowen Hua; Zhaohong Bie; Xifan Wang)
Abstract	Summary form only given. This paper proposes an efficient method for evaluating composite system reliability via subset simulation. The central idea is that a small failure probability can be expressed as a product of larger conditional probabilities, thereby turning the problem of simulating a rare failure event into several conditional simulations of more frequent intermediate failure events. In existing methods, system states are simply assessed in a binary secure/failure manner. To fit into the context of subset simulation, the adequacy of system states is parametrized with a metric based on linear programming, thus allowing for an adaptive choice of intermediate failure events. Samples conditional on these events are generated by Markov chain Monte Carlo simulation. The proposed method requires no prior information before simulation. Different models for renewable energy sources can also be accommodated. Numerical tests show that this method is significantly more efficient than standard Monte Carlo simulation, especially for simulating rare failure events.
Starting Page	1
Ending Page	1
File Size	76883
Page Count	1
File Format	PDF
ISBN	9781467380409
DOI	10.1109/PESGM.2015.7285928
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Interconnected systems Reliability engineering Electrical engineering Monte Carlo methods Insulation
Content Type	Text
Resource Type	Article

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