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Application of artificial neural networks to nuclear power plant transient diagnosis

Author

Listed:
  • Santosh, T.V.
  • Vinod, Gopika
  • Saraf, R.K.
  • Ghosh, A.K.
  • Kushwaha, H.S.

Abstract

A study on various artificial neural network (ANN) algorithms for selecting a best suitable algorithm for diagnosing the transients of a typical nuclear power plant (NPP) is presented. NPP experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems, etc. In case of any undesired plant condition generally known as initiating event (IE), the operator has to carry out diagnostic and corrective actions. The objective of this study is to develop a neural network based framework that will assist the operator to identify such initiating events quickly and to take corrective actions. Optimization study on several neural network algorithms has been carried out. These algorithms have been trained and tested for several initiating events of a typical nuclear power plant. The study shows that the resilient-back propagation algorithm is best suitable for this application. This algorithm has been adopted in the development of operator support system. The performance of ANN for several IEs is also presented.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:10:p:1468-1472
    DOI: 10.1016/j.ress.2006.10.009
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    Cited by:

    1. Tianhao Zhang & Qianqian Jia & Chao Guo & Xiaojin Huang, 2023. "Abnormal Event Detection in Nuclear Power Plants via Attention Networks," Energies, MDPI, vol. 16(18), pages 1-16, September.
    2. Pantula, Priyanka D. & Mitra, Kishalay, 2020. "Towards Efficient Robust Optimization using Data based Optimal Segmentation of Uncertain Space," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    3. Ardvin Kester S. Ong & Jelline C. Cuales & Jose Pablo F. Custodio & Eisley Yuanne J. Gumasing & Paula Norlene A. Pascual & Ma. Janice J. Gumasing, 2023. "Investigating Preceding Determinants Affecting Primary School Students Online Learning Experience Utilizing Deep Learning Neural Network," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    4. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    5. Navneet Singh Bhangu & G. L. Pahuja & Rupinder Singh, 2017. "Enhancing reliability of thermal power plant by implementing RCM policy and developing reliability prediction model: a case study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1923-1936, November.
    6. 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.
    7. Martinez-Martinez, Sinuhe & Messai, Nadhir & Jeannot, Jean-Philippe & Nuzillard, Danielle, 2015. "Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 50-57.
    8. 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.
    9. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Kate Nicole M. Tayao & Klint Allen Mariñas & Irene Dyah Ayuwati & Reny Nadlifatin & Satria Fadil Persada, 2022. "Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines," IJERPH, MDPI, vol. 19(22), pages 1-24, November.
    10. Yang, Jaemin & Kim, Jonghyun, 2020. "Accident diagnosis algorithm with untrained accident identification during power-increasing operation," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    11. 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).
    12. Guikema, Seth D., 2009. "Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 855-860.

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