In this work, Probabilistic Safety Assessment (PSA) is used to evaluate Allowed Outage Times (AOT) and Surveillance Test Intervals (STI) extensions for three Angra 1 nuclear power plant safety systems. The interest in such an analysis lies on the fact that PSA comprises a risk-based tool for safety evaluation and has been increasingly applied to support both the regulatory and the operational decision-making processes. Regarding Angra 1, among other applications, PSA is meant to be an additional method that can be used by the utility to justify Technical Specification relaxation to the Brazilian regulatory body. The risk measure used in this work is the Core Damage Frequency, obtained from the Angra 1 Level 1 PSA study. AOT and STI extensions are evaluated for the Safety Injection, Service Water and Auxiliary Feedwater Systems using the SAPHIRE code. In order to compensate for the risk increase caused by the extensions, compensatory measures as (1) test of redundant train prior to entering maintenance and (2) staggered test strategy are proposed. Results have shown that the proposed AOT extensions are acceptable for two of the systems with the implementation of compensatory measures whereas STI extensions are acceptable for all three systems. 1. Introduction Traditionally, Technical Specifications (TS) such as limiting conditions of operation, which include system/component AOT and STI, have been established based only on deterministic analysis [1, 2] and engineering judgment [2]. However, the experience with plant operation indicates that some elements of the requirements may be unnecessarily restrictive, and a few may not be conducive to safety [2], stressing the need to review them based on probabilistic models capable of assessing the incremental risks associated with their modifications. In the last decades, PSAs have been elaborated and used not only to support risk-informed regulation but also to evaluate new plant designs, among other applications. Due to its broad modeling capability, which includes system functions and common-cause failure events (CCF), PSA is especially suitable for the analysis of TS modifications. Risk-based methods to improve TS requirements are meant to (1) evaluate the risk impact of TS modifications in such a way as to objectively justify them and (2) provide risk-based information for the regulatory decision-making process [1]. This work presents an evaluation of AOT and STI extensions for three Angra 1 safety systems [3] through the use of its PSA Level 1 study, namely (1) Safety Injection System (SIS), (2) Service
References
[1]
S. P. Sirén and K. E. J？nk？ll？, “Risk-informed optimization of allowed outage times at Loviisa NPP,” in Proceedings of the 8th International Conference on Probabilistic Safety Assessment and Management (IAPSAM '06), New Orleans, Fla, USA, 2006.
[2]
P. Samanta, I. Kim, T. Mankamo, et al., “Handbook of methods for risk-based analyses of technical specifications,” US Nuclear Regulatory Commission NUREG/CR-6141, Washington, DC, USA, 1994.
[3]
Eletrobrás Termonuclear, “Final Safety Analysis Report CNAAA,” Unity 1. Rev. 33, Rio de Janeiro, Brazil, 2005.
[4]
NRC, “Systems analysis programs for hands-on integrated reliability evaluations (SAPHIRE),” US Nuclear Regulatory Commission NUREG/CR-6116, Washington, DC, USA, 1998.
[5]
M. Modarres, Risk Analysis in Engineering: Techniques, Tools, and Trends, Taylor & Francis, Boca Raton, Fla, USA, 2006.
[6]
NRC, “Regulatory guide 1.174: an approach for using PSA in risk-informed decisions on plant-specific changes to the licensing basis,” US Nuclear Regulatory Commission, Washington, DC, USA, 2002.
[7]
S. Martorell, G. Serradell, G. Verdú, and P. Samanta, “Probabilistic analysis of the interaction between allowed outage time and surveillance test interval requirements,” in IAEA Advances in Reliability Analysis and Probabilistic Safety Assessment for Nuclear Reactors. IAEA-TECDOC-737, pp. 204–212, Vienna, Austria, 1994.
[8]
K. N. Fleming and R. P. Murphy, “Lessons learned in applying PSA methods to technical specifications optimization,” in IAEA Advances in Reliability Analysis and Probabilistic Safety Assessment for Nuclear Reactors. IAEA-TECDOC-737, pp. 185–191, Vienna, Austria, 1994.
[9]
T. Mankamo, I. S. Kim, and P. K. Samanta, “Risk-based evaluation of allowed outage times (AOTs): considering risk of shutdown,” in IAEA. Advances in Reliability Analysis and Probabilistic Safety Assessment for Nuclear Reactors. IAEA-TECDOC-737, pp. 216–222, Vienna, Austria, 1994.
[10]
S. A. Martorell, V. G. Serradell, and P. K. Samanta, “Improving allowed outage time and surveillance test interval requirements: a study of their interactions using probabilistic methods,” Reliability Engineering and System Safety, vol. 47, no. 2, pp. 119–129, 1995.
[11]
M. ？epin and B. Mavko, “Probabilistic safety assessment improves surveillance requirements in technical specifications,” Reliability Engineering and System Safety, vol. 56, no. 1, pp. 69–77, 1997.
[12]
M. L. Shooman, Probabilistic Reliability: An Engineering Approach, Robert E Krieger, Malabar, Fla, USA, 1990.
[13]
J. K. Vaurio, “On time-dependent availability and maintenance optimization of standby units under various maintenance policies,” Reliability Engineering and System Safety, vol. 56, no. 1, pp. 79–89, 1997.
[14]
I. B. Wall, J. J. Haugh, and D. H. Worlege, “Recent applications of PSA for managing nuclear power plant safety,” Progress in Nuclear Energy, vol. 39, no. 3-4, pp. 367–425, 2001.
[15]
M. ？epin and S. Martorell, “Evaluation of allowed outage time considering a set of plant configurations,” Reliability Engineering and System Safety, vol. 78, no. 3, pp. 259–266, 2002.
[16]
S. Martorell, A. Sánchez, S. Carlos, and V. Serradell, “Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs,” Annals of Nuclear Energy, vol. 29, no. 2, pp. 147–168, 2002.
[17]
D. Kancev and M. Cepin, “A new method for explicit modelling of single failure event within different common cause failure groups,” Reliability Engineering and System Safety, vol. 103, pp. 84–93, 2012.
[18]
J. K. Vaurio, “Common cause failure probabilities in standby safety system fault tree analysis with testing—scheme and timing dependencies,” Reliability Engineering and System Safety, vol. 79, no. 1, pp. 43–57, 2003.
[19]
T. M. J. Kivirinta and K. E. J？nk？ll？, “Determining risk-balanced allowed outage times for Loviisa power plant,” in Proceedings of the 8th International Conference on Probabilistic Safety Assessment and Management (IAPSAM '06), New Orleans, Fla, USA, 2006.
[20]
S. Martorell, S. Carlos, J. F. Villanueva et al., “Use of multiple objective evolutionary algorithms in optimizing surveillance requirements,” Reliability Engineering and System Safety, vol. 91, no. 9, pp. 1027–1038, 2006.
[21]
X. He, J. Tong, and J. Chen, “Maintenance risk management in Daya Bay nuclear power plant: PSA model, tools and applications,” Progress in Nuclear Energy, vol. 49, no. 1, pp. 103–112, 2007.
[22]
S. Cho and J. Jiang, “Analysis of surveillance test interval by Markov process for SDS1 in CANDU nuclear power plants,” Reliability Engineering and System Safety, vol. 93, no. 1, pp. 1–13, 2008.
[23]
NRC, “Procedures for treating common cause failures in safety and reliability studies,” US Nuclear Regulatory Commission NUREG/CR-4780, Washington, DC, USA, 1987.
[24]
NRC, “Regulatory guide 1.177: an approach for plant-specific risk-informed decision-making: technical specifications,” US Nuclear Regulatory Commission, Washington, DC, USA, 1998.
[25]
NRC, “Guidelines on modeling common-cause failures in probabilistic risk assessment,” US Nuclear Regulatory Commission NUREG/CR-5485, Washington, DC, USA, 1998.
[26]
S. M. Ross, Introduction to Probability Models, Academic Press, San Diego, Calif, USA, 1993.
[27]
J. K. Vaurio, “Developments in importance measures for risk-informed ranking and other applications,” in Proceedings of the 8th International Conference on Probabilistic Safety Assessment and Management (IAPSAM '06), New Orleans, Fla, USA, 2006.
[28]
R. W. Youngblood, “Risk significance and safety significance,” Reliability Engineering and System Safety, vol. 73, no. 2, pp. 121–136, 2001.
[29]
NRC, “Study of operational risk-based configuration control,” US Nuclear Regulatory Commission NUREG/CR-5641, BNL-NUREG-52261, Washington, DC, USA, 1991.
[30]
NRC, “Handbook of parameter estimation for probabilistic risk assessment,” US Nuclear Regulatory Commission NUREG/CR-6823, SAND2003-3348P, Washington, DC, USA, 2003.
[31]
NRC, “Risk-informed decision-making: technical specifications. Standard review plant,” US Nuclear Regulatory Commission NUREG-0800, Washington, DC, USA, 2007.
[32]
S. Epstein and A. Rauzy, “Can we trust PRA?” Reliability Engineering and System Safety, vol. 88, no. 3, pp. 195–205, 2005.
[33]
NRC, “Probabilistic risk assessment procedures guide,” US Nuclear Regulatory Commission NUREG/CR-2300, Washington, DC, USA, 1982.
[34]
R. E. Bryant, “Graph based algorithms for Boolean function manipulation,” IEEE Transactions on Computers, vol. 35, no. 8, pp. 677–691, 1986.
[35]
L. F. S. Oliveira, P. F. Frutuoso e Melo, J. E. P. Lima, and I. L. Stal, “An application of the explicit method for analysing intersystem dependencies in the evaluation of event trees,” Nuclear Engineering and Design, vol. 90, no. 1, pp. 25–41, 1985.
[36]
M. ？epin, “Analysis of truncation limit in probabilistic safety assessment,” Reliability Engineering and System Safety, vol. 87, no. 3, pp. 395–403, 2005.
[37]
NRC, “Probabilistic safety analysis procedures guide,” US Nuclear Regulatory Commission NUREG/CR-2815, Washington, DC, USA, 1985.
