Availability analysis of safety-critical and control systems of NPP using stochastic modeling

• Published in: Annals of nuclear energy Vol. 147; p. 107657
• Main Authors: Kumar, Pramod, Kumar Singh, Lalit, Chaudhari, Nilesh, Kumar, Chiranjeev
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2020
• Subjects: Availability; Markov models; Nuclear power plant; Petri nets; Safety critical systems; Availability; MTTR; SCS; Petri nets; (S)PN; Safety critical systems; NPP; FTA; (D)RBD; Nuclear power plant; CBS; Markov models; DFWCS
• ISSN: 0306-4549; 1873-2100
• DOI: 10.1016/j.anucene.2020.107657

Non-functional requirements are essentially important and play vital role for applications ranging from safety-critical systems (SCS) to simple gaming applications to ensure their quality. SCS demands not only for safe and reliable systems but systems those remain safe and available while under attacks. Availability analysis approaches include, but are not limited to cluster technique, Markov based chain models, Reliability Block Diagrams (RBD), Fault Tree Analysis (FTA) and Flow Network. The classical approaches fail to account for the comprehensive and accurate analysis of the diverse characteristics such as temporal behavior of systems, uncertainty in system behavior and failure data, functional dependencies among components and multiple failure modes for components or systems. This paper presents a novel approach for the availability analysis of a Digital Feed Water Control System (DFWCS) of nuclear power plant, which considers the maintenance and repair of the main-steam safety valves. The approach will be useful when no operational profile data is available for that. The system has been modeled using Stochastic Petri Net capturing all the system requirements along with the partial failures of its subsystems and common-cause failures and analyzed using TimeNet tool. The proposed methodology proves to be efficient and overcomes the limitations of the traditional approaches and the Markov model approach as it computes the state-transition probabilities, rather than assuming or qualitatively assessing it.