A new approach for dynamic reliability analysis of reactor protection system for HPR1000

•A new approach for dynamic reliability analysis of the RPS for HPR1000 was proposed.•The dynamic prediction analysis for the RPS was conducted by the approach.•The dynamic sensitivity analysis for the RPS was conducted by the approach.•Three dynamic characteristics of the RPS were analyzed by the a...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 234; p. 109147
Main Authors: Xu, Jintao, Gui, Maolei, Ding, Rui, Dai, Tao, Zheng, Mengyan, Men, Xinhong, Meng, Fanpeng, Yu, Tao, Sui, Yang
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.06.2023
Subjects:
Dynamic bayesian network model
Dynamic fault tree
Dynamic reliability analysis
New bayesian inference algorithm
Reactor protection system
Dynamic reliability analysis
Dynamic fault tree
Reactor protection system
New bayesian inference algorithm
Dynamic bayesian network model
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:•A new approach for dynamic reliability analysis of the RPS for HPR1000 was proposed.•The dynamic prediction analysis for the RPS was conducted by the approach.•The dynamic sensitivity analysis for the RPS was conducted by the approach.•Three dynamic characteristics of the RPS were analyzed by the approach. Traditional static methods are mostly adopted to analyze the reliability of reactor protection system (RPS) for nuclear power plant (NPP). However, they cannot characterize its dynamic interaction, time correlation, and uncertainty. In order to solve this problem, dynamic fault tree (DFT) analysis was firstly utilized to create the DFT for the RPS to characterize its dynamic interaction. Then, dynamic Bayesian network (DBN) was used to create the DBN model based upon the DFT for the RPS to characterize its dynamic interaction, time correlation, and uncertainty. Furthermore, Latin hypercube sampling (LHS) was employed to define a new Bayesian inference algorithm. Finally, the defined algorithm was applied in a RPS for Hualong-1 (HPR1000) in East China to conduct its dynamic prediction and sensitivity analyses through the Bayesian forward and backward inferences, and a new approach for dynamic reliability analysis of the RPS for HPR1000 was proposed. The research results showed that the proposed approach could be used for the dynamic reliability analysis of the RPS.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2023.109147