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AN OVERVIEW OF RISK QUANTIFICATION ISSUES FOR DIGITALIZED NUCLEAR POWER PLANTS USING A STATIC FAULT TREE

  • 발행 : 2009.08.31

초록

Risk caused by safety-critical instrumentation and control (I&C) systems considerably affects overall plant risk. As digitalization of safety-critical systems in nuclear power plants progresses, a risk model of a digitalized safety system is required and must be included in a plant safety model in order to assess this risk effect on the plant. Unique features of a digital system cause some challenges in risk modeling. This article aims at providing an overview of the issues related to the development of a static fault-tree-based risk model. We categorize the complicated issues of digital system probabilistic risk assessment (PRA) into four groups based on their characteristics: hardware module issues, software issues, system issues, and safety function issues. Quantification of the effect of these issues dominates the quality of a developed risk model. Recent research activities for addressing various issues, such as the modeling framework of a software-based system, the software failure probability and the fault coverage of a self monitoring mechanism, are discussed. Although these issues are interrelated and affect each other, the categorized and systematic approach suggested here will provide a proper insight for analyzing risk from a digital system.

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참고문헌

  1. Kang, H.G, et al., Survey of the Advanced Designs of Safety-Critical Digital Systems from the PSA Viewpoint, Korea Atomic Energy Research Institute, KAERI/AR-00669/2003, 2003
  2. Shin, H.G, Nam, S.G, Sohn, S.D and Chang, H.S, “Development of an advanced digital reactor protection system using diverse dual processors to prevent commonmode failure,” Nuclear Technology, Vol.141, 2003 https://doi.org/10.13182/NT03-A3347
  3. Seong, P.H, et al., Reliability and Risk Issues in Large Scale Safety-critical Digital Control Systems, Springer London, 2008
  4. HSE, The use of computers in safety-critical applications, London, HSE Books, 1998
  5. Lu, L and Jiang, J, “Probabilistic Safety Assessment for Instrumentation and Control Systems in Nuclear Power Plants: An Overview,” Journal of Nuclear Science and Technology, Vol. 41, No.3, 2004 https://doi.org/10.3327/jnst.41.323
  6. Kang, H.G and Sung, T, “An analysis of safety-critical digital systems for risk-informed design,” Reliability Engineering and Systems Safety, Vol. 78, No. 3, 2002 https://doi.org/10.1016/S0951-8320(02)00176-X
  7. Chu, T.L, Martinez-Guridi, Yue, M, Lehner, J, and Samanta, P, “Traditional Probabilistic Risk Assessment Methods for Digital Systems,” NUREG/CR-6962, October 2008
  8. US MIL-HDBK-217, Reliability Prediction of Electronic Equipment, version F, DOD, USA, 1991
  9. Bellcore Technical Ref. TR-TSY-000332, Reliability prediction procedure for electronic equipment: issue 6, 1997
  10. ERPD-97, Electronic Parts Reliability Data, RAC, 1996
  11. Jung, H.S, Jang, S.C, Kim, M.C, Jun, S.T, “Analysis of Hardware Reliabilities for NPP Digital I&C Equipment Predicted by Various Methods,” International congress on advances in nuclear power plants; ICAPP '03, 2003
  12. OECD/NEA, Computer-Based Systems Important to Safety (COMPSIS) Project: 3 Years of Operation (2005-2007), Draft Report, NEA/CSNI/R(2008). 2008
  13. Lee, D.Y, Choi, J.G, and Lyou, Y, “A Safety Assessment Methodology for a Digital Reactor Protection System,” International Journal of Control, Automation, and Systems, Vol. 4, No. 1, 2006
  14. White, R.M and Boettcher, D.B, “Putting Sizewell B digital protection in context,” Nuclear Engineering International, pp. 41-43, 1994
  15. Parnas, D.L, Asmis, G.J.K, and Madey, J, “Assessment of Safety-critical Software in Nuclear Power Plants,” Nuclear Safety, Vol. 32, No. 2., 1991
  16. Kim, M.C, Jang, S.C, and Ha, J, “Possibilities and limitations of applying software reliability growth models to safetycritical software,” Nuclear Engineering and Technology, vol.39, no.2, pp.145-148, 2007
  17. Littlewood B, Wright D, “Some conservative stopping rules for the operational testing of safety-critical software,” IEEE Trans. Software Engineering, Vol. 23, No. 11, 1997, pp. 673-685 https://doi.org/10.1109/32.637384
  18. Kang, H.G, Lim, H.G, Lee, H.J, Kim, M.C, and Jang, S.C, “A Test-Based Software Failure Probability Quantification Method for Safety-Critical Applications,” The 7th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, Operation and Safety, Seoul, Korea, October 5-9, 2008
  19. INL, Technology Roadmap on Instrumentation, Control, and Human-Machine Interface to Support DOE Advanced Nuclear Energy Programs, INL/EXT-06-11862, Idaho National lab., March 2007
  20. Dahll, G, The use of Bayesian Belief Nets in Safety Assessment of Software based System, HWP-527, Halden Project, 1998
  21. Eom, H.S, et al., Survey of Bayesian Belief Nets for Quantitative Reliability Assessment of Safety Critical Software Used in Nuclear Power Plants, Korea Atomic Energy Research Institute, KAERI/AR-594/2001, 2001
  22. Fenton, N, Neil, M, David Marques, “Using Bayesian Networks to Predict Software Defects and Reliability,” 5th International Mathematical Methods in Reliability Conference (MMR 07), July 2007
  23. Butler, R.W and Finelli, G.B, “Infeasibility of Quantifying the Reliability of Life-Critical Real-Time Software,” IEEE Transaction on Software Engineering, Vol.19, Issue 1, IEEE Press, 1993 https://doi.org/10.1109/32.210303
  24. IEEE, IEEE Standard Criteria for Digital Computers in Safety Systems of Nuclear Power Generating Stations, IEEE-7.4.3.2, 2003
  25. Kahneman, D, Slovic, P, and Tversky, A, Judgment under uncertainty: Heuristics and biases, Cambridge University Press, 1982
  26. Uusitalo, L, "Advantages and challenges of Bayesian networks in environmental modeling," Ecological modeling, Vol. 203, pp.312-318, 2007 https://doi.org/10.1016/j.ecolmodel.2006.11.033
  27. Kang, H.G, et al., The Common Cause Failure Probability Analysis on the Hardware of the Digital Protection System in Korean Standard Nuclear Power Plant, KAERI/TR-2908/2005, 2005
  28. Kaufman, L.M, Johnson, B.W, and Bechta Dugan, J, “Coverage Estimation Using Statistics of Extremes for When Testing Reveals No Failures”, IEEE Transactions on Computers, Vol. 51, No. 1, 2002 https://doi.org/10.1109/12.980013
  29. DeLong, T, Smith, D, and Johnson, B, “Dependability Metrics to Assess Safety-Critical Systems,” IEEE Transactions on Reliability, Vol. 54, No. 3, 2005 https://doi.org/10.1109/TR.2005.853567
  30. Kim, S.J, Seong, P.H, Lee, J.S, Kim, M.C, Kang, H.G, and Jang, S.C, “A Method of Fault Coverage Evaluation for Digitalized Systems in Nuclear Power Plants using Simulated Fault Injection,” Reliability Engineering and System Safety, vol.91, pp.614-623, 2005 https://doi.org/10.1016/j.ress.2005.05.002
  31. Lee, J.S, Kim, M.C, Seong, P.H, Kang, H.G, and Jang, S.C, “Evaluation of error detection coverage and fault-tolerance of digital plant protection system in nuclear power plants,” Annals of Nuclear Energy, vol.33, pp.544-554, 2006 https://doi.org/10.1016/j.anucene.2006.01.003
  32. Kang, H.G and Jang, S.C, “Application of condition-based HRA method for a manual actuation of the safety features in a nuclear power plant,” Reliability Engineering & System Safety, Vol. 91, 2006 https://doi.org/10.1016/j.ress.2005.04.007
  33. US Nuclear Regulatory Commission (USNRC), Technical basis and implementation guidelines for a technique for human event analysis (ATHEANA), Washington, D.C., NUREG-1624 Rev. 1, 2000
  34. Forester, J, Bley, D, Cooper, S, Lois, E, Siu, N, Kolaczkowski, A, and Wreathall, J, “Expert elicitation approach for performing ATHEANA quantification,” Reliability Engineering and System Safety, Vol. 83, 2004
  35. Kang, H.G, Jang, S.C, and Lim, H.G, “ATWS Frequency Quantification Focusing on Digital I&C Failures,” Journal of Korea Nuclear Society, Vol. 36, 2004

피인용 문헌

  1. The Software Reliability Evaluation of a Nuclear Controller Software Using a Fault Detection Coverage Based on the Fault Weight vol.5, pp.9, 2016, https://doi.org/10.3745/KTCCS.2016.5.9.275
  2. A novel framework for the reliability modelling of repairable multistate complex mechanical systems considering propagation relationships pp.07488017, 2018, https://doi.org/10.1002/qre.2382