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Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks

Author

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  • Santosh, T.V.
  • Srivastava, A.
  • Sanyasi Rao, V.V.S.
  • Ghosh, A.K.
  • Kushwaha, H.S.

Abstract

This paper presents the work carried out towards developing a diagnostic system for the identification of accident scenarios in 220MWe Indian PHWRs. The objective of this study is to develop a methodology based on artificial neural networks (ANNs), which assists in identifying a transient quickly and suggests the operator to initiate the corrective actions during abnormal operations of the reactor. An operator support system, known as symptom-based diagnostic system (SBDS), has been developed using ANN that diagnoses the transients based on reactor process parameters, and continuously displays the status of the reactor. As a pilot study, the large break loss of coolant accident (LOCA) with and without the emergency core cooling system (ECCS) in reactor headers has been considered. Several break scenarios of large break LOCA have been analyzed. The time-dependent transient data have been generated using the RELAP5 thermal hydraulic code assuming an equilibrium core, which conforms to a realistic estimation. The diagnostic results obtained from the ANN study are satisfactory. These results have been incorporated in the SBDS software for operator assistance. A few important outputs of the SBDS have been discussed in this paper.

Suggested Citation

  • Santosh, T.V. & Srivastava, A. & Sanyasi Rao, V.V.S. & Ghosh, A.K. & Kushwaha, H.S., 2009. "Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 759-762.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:3:p:759-762
    DOI: 10.1016/j.ress.2008.08.005
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    References listed on IDEAS

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    1. Santosh, T.V. & Vinod, Gopika & Saraf, R.K. & Ghosh, A.K. & Kushwaha, H.S., 2007. "Application of artificial neural networks to nuclear power plant transient diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1468-1472.
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    Cited by:

    1. Gómez, M.J. & Castejón, C. & García-Prada, J.C., 2016. "Automatic condition monitoring system for crack detection in rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 239-247.
    2. Peng Su & Guanjun Wang, 2022. "Reliability analysis of network systems subject to probabilistic propagation failures and failure isolation effects," Journal of Risk and Reliability, , vol. 236(2), pages 290-306, April.
    3. Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
    4. Tolo, Silvia & Tian, Xiange & Bausch, Nils & Becerra, Victor & Santhosh, T.V. & Vinod, G. & Patelli, Edoardo, 2019. "Robust on-line diagnosis tool for the early accident detection in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 110-119.
    5. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    6. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Oh, ChoHwan & Lee, Jeong Ik, 2020. "Real time nuclear power plant operating state cognitive algorithm development using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 198(C).

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