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Human error probability quantification for NPP post-accident analysis using Cognitive-Based THERP method

delete2020-05-01
delete19
PRE
AI
A
Ahmad Ramezani
T
Tooraj Nazari *
A
Ataollah Rabiee
K
Kamal Hadad
M
Mohammadjavad Faridafshin
DOI:10.1016/j.pnucene.2020.103281delete
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摘要

摘要

En 中文
Human Reliability Analysis (HRA) studies human actions from the safety point of view, modeling the most important of them and assessing their probabilities. The Technique for Human Error Rate Prediction (THERP) is a classical HRA approach that models Human Error Probability (HEP) for a specific action regardless of the human cognitive process. Another HRA method is Human Cognitive Reliability (HCR) that expresses the non-response probability as a function of available time regarding the performance type and ignores other affecting factors. In this study, the strengths of the Deterministic Safety Analysis (DSA), THERP and HCR are integrated to form a systematic framework introduced as Cognitive Based THERP (CB-THERP) method to calculate HEP for the Nuclear Power Plant (NPP) post-accident analysis. The calculated values of HEP during the Total Loss of Feedwater (TLFW) accident reveal that a better Human-Machine Interface (HMI) condition reduces HEP. However, operators' higher training level compensates even the worst HMI. In addition, the HMI condition has the greatest impact on an averaged-knowledge Main Control Room (MCR) operator and then an expert and a novice. Dependency almost doesn't influence the worst HMI condition and it much affects highly trained operators when the HMI condition is excellent. Also, the calculated HEP values from the Standardized Plant Analysis Risk-Human Reliability (SPAR-H) and CB-THERP methods for different cases of the scenario have similar trend while THERP calculates a fixed value for them.
Keyword:
Human reliability analysis
DSA
Cognitive-based THERP
SPAR-H
TLFW
Main control room personnel
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期刊

Progress in Nuclear Energy 封面图
Progress in Nuclear Energy
IF:
3.2
论文数:
5.6K
被引数:
10.0K

机构

S
Shiraz University
学者数:
8.1K
论文数: 7.5K
被引数: 7.4K
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