Research on condition assessment of nuclear power systems based on fault severity and fault harmfulness

Haotong Wang, Yanjun Li, Chaojing Lin, Siyuan Yang, Guolong Li, Shengdi Sun, Ye Tian and Jianxin Shi

Energy, 2024, vol. 311, issue C

Abstract: Obtaining real time quantitative condition assessment results is a focus in the field of nuclear power system PHM. The existing researches on the nuclear power system condition assessment based on the Multi-Criteria Decision-Making framework do not consider the faults perniciousness differences. This leads to the assessment results relying on the system status deviation degrees(severity), and the weighting and aggregation processes cannot effectively reflect the differences in the consequences and risks of different faults, which represent the faults harmfulness. A quantitative assessment method based on fault severity and fault harmfulness is proposed to address this issue. The novel method quantifies the fault severity based on the similarity principle while diagnosing system faults, and then analyzes the devices mainly affected by various faults to quantifies the harmfulness. Combining the system status deviation degrees(severity), the devices and parameters importance, and the differences in faults harmfulness, the novel method aggregates comprehensive system condition assessment results. Based on a widely recognized nuclear power system faults dataset, the novel method was compared with other methods. The conclusion is that the novel method considers the differences in the faults harmfulness, resulting in more reasonable assessment results and avoiding insufficient or excessive warnings.

Keywords: NPP; Pressurized water reactor; PHM; Machine learning; Condition assessment (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:311:y:2024:i:c:s0360544224031724

DOI: 10.1016/j.energy.2024.133396

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