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

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.

Automated summary

Traditional static methods are not sufficient to analyze the dynamic interaction, time correlation, and uncertainty of reactor protection system (RPS) for nuclear power plant (NPP). This paper proposes a new approach using dynamic fault tree (DFT), dynamic Bayesian network (DBN), and Latin hypercube sampling (LHS) to analyze the dynamic reliability of RPS. The proposed approach is applied to Hualong-1 (HPR1000) RPS for dynamic prediction and sensitivity analyses, suggesting its effectiveness for dynamic reliability analysis of RPS.