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Review on the Application of Living PSA in Nuclear Power

Author

Listed:
  • Min Zhang

    (China Nuclear Power Engineering Co., Ltd., Beijing 100840, China)

  • Zhao Xu

    (China Nuclear Power Engineering Co., Ltd., Beijing 100840, China)

  • Guichang Zhang

    (School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China)

  • Binbin Wang

    (School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China)

  • Bin Zhang

    (School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China)

  • Yilong Liu

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

With the increasing standards of safety management in nuclear power plants, Living Probabilistic Safety Assessment (Living PSA) technology has begin to play an increasingly important role in their operation. This paper aims to provide an overview of the application and development of Living Probabilistic Safety Assessment (Living PSA) technology in nuclear power plant safety monitoring and risk assessment, examining the key technologies and future challenges. Initially, we summarize the current safety needs in regard to nuclear power, examine the policy on configuration risk management technology for nuclear power plants, and outline its importance and development process in nuclear power plant safety management. Subsequently, we discuss the basic principle of Living PSAs and the working method of risk monitoring based on Living PSAs, including information monitoring data collection, online identification, real-time model updating, and risk calculation. Within the Living PSA framework, model development is not merely about creating a theoretical or static representation; it is a dynamic and ongoing process that involves a deep understanding and precise simulation of the behavior of nuclear power plant systems and components. This represents the main research efforts in Living PSAs at present. Additionally, this paper identifies the key technologies of Living PSAs in an in-depth manner, such as the reliability-model-updating technology and model building in dynamic reliability analyses, including the fault tree model, multi-layer flow model, GO-FLOW model etc. The paper lists the work of some scholars in this area in recent years, which helps readers and researchers to clearly understand the current progress of Living PSA technologies in terms of model establishment and updating. Finally, the paper summarizes the challenges and future development of Living PSA and emphasizes the possible problems in data quality, human factor engineering, and the development of Living PSA technologies in the future. In the future, Living PSAs will provide more solid support for the realization of safer and more economical methods of operating nuclear power plants.

Suggested Citation

  • Min Zhang & Zhao Xu & Guichang Zhang & Binbin Wang & Bin Zhang & Yilong Liu, 2024. "Review on the Application of Living PSA in Nuclear Power," Energies, MDPI, vol. 17(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5578-:d:1516604
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    References listed on IDEAS

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    1. Kessides, Ioannis N., 2012. "The future of the nuclear industry reconsidered: Risks, uncertainties, and continued promise," Energy Policy, Elsevier, vol. 48(C), pages 185-208.
    2. Doguc, Ozge & Ramirez-Marquez, Jose Emmanuel, 2009. "A generic method for estimating system reliability using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 542-550.
    3. Mustakimah Mohamed & Nur Diyana Zakuan & Tengku Nur Adibah Tengku Hassan & Serene Sow Mun Lock & Azmi Mohd Shariff, 2024. "Global Development and Readiness of Nuclear Fusion Technology as the Alternative Source for Clean Energy Supply," Sustainability, MDPI, vol. 16(10), pages 1-37, May.
    4. Chunyuan Zhang & Pengyu Chen & Fangling Jiang & Jinsen Xie & Tao Yu, 2023. "Fault Diagnosis of Nuclear Power Plant Based on Sparrow Search Algorithm Optimized CNN-LSTM Neural Network," Energies, MDPI, vol. 16(6), pages 1-17, March.
    5. Kessides, Ioannis N., 2012. "The future of the Nuclear industry reconsidered : risks, uncertainties, and continued potential," Policy Research Working Paper Series 6112, The World Bank.
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