Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2020, Vol. 31 ›› Issue (10): 101 doi: 10.1007/s41365-020-00814-6

• NUCLEAR ENERGY SCIENCE AND ENGINEERING • Previous Articles     Next Articles

Bayesian belief based model for reliability improvement of the digital reactor protection system

Hanaa Torkey1 • Amany S. Saber2 • Mohamed K. Shaat2 • Ayman El-Sayed1 • Marwa A. Shouman1   

  1. 1 Faculty of Electronic Engineering, Menoufia University, Cairo, Egypt
    2 Nuclear Research Center, Egyptian Atomic Energy Authority, Cairo, Egypt
  • Received:2020-05-30 Revised:2020-08-21 Accepted:2020-08-22
  • Contact: Amany S. Saber
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Hanaa Torkey, Amany S. Saber, Mohamed K. Shaat, Ayman El-Sayed, Marwa A. Shouman. Bayesian belief based model for reliability improvement of the digital reactor protection system.Nuclear Science and Techniques, 2020, 31(10): 101     doi: 10.1007/s41365-020-00814-6

Abstract: The digital reactor protection system (RPS) is one of the most important digital instrumentation and control (I&C) systems utilized in nuclear power plants (NPPs). It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor. Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings. The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP, based on a Bayesian Belief Network (BBN) model. The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components. Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system. A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability. The results showed that the highest availability obtained using the proposed method was 0.9999998. There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules, which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.

Key words: Nuclear power plants, Reactor protection system, Bayesian belief network