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Scenario clustering and dynamic probabilistic risk assessment

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  • Mandelli, Diego
  • Yilmaz, Alper
  • Aldemir, Tunc
  • Metzroth, Kyle
  • Denning, Richard

Abstract

A challenging aspect of dynamic methodologies for probabilistic risk assessment (PRA), such as the Dynamic Event Tree (DET) methodology, is the large number of scenarios generated for a single initiating event. Such large amounts of information can be difficult to organize for extracting useful information. Furthermore, it is not often sufficient to merely calculate a quantitative value for the risk and its associated uncertainties. The development of risk insights that can increase system safety and improve system performance requires the interpretation of scenario evolutions and the principal characteristics of the events that contribute to the risk. For a given scenario dataset, it can be useful to identify the scenarios that have similar behaviors (i.e., identify the most evident classes), and decide for each event sequence, to which class it belongs (i.e., classification). It is shown how it is possible to accomplish these two objectives using the Mean-Shift Methodology (MSM). The MSM is a kernel-based, non-parametric density estimation technique that is used to find the modes of an unknown data distribution. The algorithm developed finds the modes of the data distribution in the state space corresponding to regions with highest data density as well as grouping the scenarios generated into clusters based on scenario temporal similarities. The MSM is illustrated using the data generated by a DET algorithm for the analysis of a simple level/temperature controller and reactor vessel auxiliary cooling system.

Suggested Citation

  • Mandelli, Diego & Yilmaz, Alper & Aldemir, Tunc & Metzroth, Kyle & Denning, Richard, 2013. "Scenario clustering and dynamic probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 146-160.
    • Handle: RePEc:eee:reensy:v:115:y:2013:i:c:p:146-160
    DOI: 10.1016/j.ress.2013.02.013
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    References listed on IDEAS

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    1. Zheng, Xiaoyu & Tamaki, Hitoshi & Sugiyama, Tomoyuki & Maruyama, Yu, 2022. "Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
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    5. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    6. Markéta Horejšová & Sebastiano Vitali & Miloš Kopa & Vittorio Moriggia, 2020. "Evaluation of scenario reduction algorithms with nested distance," Computational Management Science, Springer, vol. 17(2), pages 241-275, June.
    7. Kaneko, Fujio & Yuzui, Tomohiro, 2023. "Novel method of dynamic event tree keeping the number of simulations in risk analysis small," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Ibánez, L. & Hortal, J. & Queral, C. & Gómez-Magán, J. & Sánchez-Perea, M. & Fernández, I. & Meléndez, E. & Expósito, A. & Izquierdo, J.M. & Gil, J. & Marrao, H. & Villalba-Jabonero, E., 2016. "Application of the Integrated Safety Assessment methodology to safety margins. Dynamic Event Trees, Damage Domains and Risk Assessment," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 170-193.
    9. Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    11. Hu, Yunwei & Parhizkar, Tarannom & Mosleh, Ali, 2022. "Guided simulation for dynamic probabilistic risk assessment of complex systems: Concept, method, and application," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    12. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Rebollo, M.J. & Queral, C. & Jimenez, G. & Gomez-Magan, J. & Meléndez, E. & Sanchez-Perea, M., 2016. "Evaluation of the offsite dose contribution to the global risk in a Steam Generator Tube Rupture scenario," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 32-48.
    14. Maljovec, D. & Liu, S. & Wang, B. & Mandelli, D. & Bremer, P.-T. & Pascucci, V. & Smith, C., 2016. "Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 262-276.
    15. Haider, Sajjad & Rizvi, Rida e Zahra & Walewski, John & Schegner, Peter, 2022. "Investigating peer-to-peer power transactions for reducing EV induced network congestion," Energy, Elsevier, vol. 254(PB).

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