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Risk-Informed Intelligent Design Optimization for Nuclear Power Plants Using Probabilistic Safety Analysis

Author

Listed:
  • Ming Wang

    (China Nuclear Power Engineering Co., Ltd, State Key Laboratory of Nuclear Power Safety Technology and Equipment)

  • Jiaoshen Xu

    (China Nuclear Power Engineering Co., Ltd, State Key Laboratory of Nuclear Power Safety Technology and Equipment)

  • Bing Zhang

    (China Nuclear Power Engineering Co., Ltd, State Key Laboratory of Nuclear Power Safety Technology and Equipment)

Abstract

This study presents a systematic framework for enhancing nuclear power plant safety through intelligent design optimization, guided by Probabilistic Safety Assessment (PSA) importance analysis results. Taking the Internal Event Level 1 PSA model of the Hua-long Pressurized Reactor (HPR1000) nuclear power plant as a reference, the methodology identifies high-priority components for intelligent system upgrades. A case analysis demonstrates the potential for significant risk reduction through the implementation of advanced systems, including prognostic health management for steam isolation valves in the Secondary Passive Heat Removal System (ASP), as well as the incorporation of smart substation technologies to address Loss of Off-site Power (LOOP) risks. These targeted upgrades correspond to critical components identified through PSA, resulting in quantifiable improvements in plant safety metrics. The proposed framework establishes a risk-informed decision matrix that supports the prioritization of intelligent design investments, offering nuclear operators quantitative guidance for maximizing safety benefits. The approach is validated through system modeling of the HPR1000, confirming its applicability for next-generation nuclear reactor safety enhancement.

Suggested Citation

  • Ming Wang & Jiaoshen Xu & Bing Zhang, 2026. "Risk-Informed Intelligent Design Optimization for Nuclear Power Plants Using Probabilistic Safety Analysis," Springer Series in Reliability Engineering,, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-22873-4_8
    DOI: 10.1007/978-3-032-22873-4_8
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