A Petri net model-based resilience analysis of nuclear power plants under the threat of natural hazards

Yan, Rundong; Dunnett, Sarah; Andrews, John

Abstract

Due to global climate change, nuclear power plants are increasingly exposed to the threats of extreme natural disasters. In this paper, a resilience engineering approach is applied to tackle all aspects of nuclear safety, spanning from design, operation, and maintenance to accident response and recovery, in the case of high-impact low-probability events. Petri net models are developed to simulate the losses caused by extreme events, the health states of relevant systems, mitigation processes, and the recovery and maintenance processes. The method developed is applied to assess the resilience of a single-unit pressurised heavy water reactor under the threat of three possible external events. Possible loss of coolant accidents and station blackout accidents caused by the events are considered. With the aid of the models developed, both the influence of stochastic deterioration and the impact of external events on the resilience of the reactor can be assessed quantitatively. The simulation results show that the method can comprehensively describe the resilience of nuclear power plants against various disruptive events. It is also found that the stochastic deterioration that does not directly affect the operation of nuclear reactors is critical to the resilience of reactors.

Citation

Yan, R., Dunnett, S., & Andrews, J. (2023). A Petri net model-based resilience analysis of nuclear power plants under the threat of natural hazards. Reliability Engineering and System Safety, 230, Article 108979. https://doi.org/10.1016/j.ress.2022.108979

• Journal Article Type: Article
• Acceptance Date: Nov 11, 2022
• Online Publication Date: Nov 13, 2022
• Publication Date: 2023-02
• Deposit Date: Nov 17, 2022
• Publicly Available Date: Nov 17, 2022
• Journal: Reliability Engineering and System Safety
• Print ISSN: 0951-8320
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 230
• Article Number: 108979
• DOI: https://doi.org/10.1016/j.ress.2022.108979
• Keywords: Industrial and Manufacturing Engineering; Safety, Risk, Reliability and Quality
• Public URL: https://nottingham-repository.worktribe.com/output/13750304
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0951832022005944?via%3Dihub