Abstract
Westinghouse Electric Company is in the process of completing the Generic Design Assessment (GDA) phase of licensing its AP1000® (®AP1000 is a trademark or registered trademark of Westinghouse Electric Company LLC, its affiliates and/or its subsidiaries in the United States of America and may be registered in other countries throughout the world. All rights reserved. Unauthorized use is strictly prohibited. Other names may be trademarks of their respective owners.) Pressurized Water Reactor (PWR) nuclear power plant in the United Kingdom. To address a Human Factors (HF) GDA issue identified in an earlier phase of the GDA process, the Westinghouse HF team updated the human error analysis methodology to comport with lessons learned and advancements in the field of human reliability analysis (HRA). NUREG-2114 [1] provided a cognitive framework as an update to the psychological basis for HRA, but did not provide specific guidance to analysts on how to use this framework in performing HRA. This paper describes how the Westinghouse HF team adapted the cognitive framework in NUREG-2114 for application in the human error analyses performed to resolve the HF GDA issue. Westinghouse HF determined that the adapted cognitive framework was useful for identifying potential human errors in the task analysis and identifying potential design improvements. Results from using NUREG-2114 to inform human error analysis and recommendations for additional development are discussed.
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Notes
- 1.
The Westinghouse process for conducting HRA in the UK involves the Human Factors organization performing the qualitative analysis, and the Probabilistic Risk Analysis (PRA) discipline performing the HRA quantification and input into the overall Probabilistic Safety Assessment (PSA) model. For this reason, Westinghouse uses the term HEA to refer to the qualitative portion of the human reliability analysis.
References
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Whaley, A.M. (2018). Adapting the Cognitive Framework of NUREG-2114 for Use in Human Error Analysis for AP1000 Plant Licensing in the United Kingdom. In: Boring, R. (eds) Advances in Human Error, Reliability, Resilience, and Performance. AHFE 2017. Advances in Intelligent Systems and Computing, vol 589. Springer, Cham. https://doi.org/10.1007/978-3-319-60645-3_29
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DOI: https://doi.org/10.1007/978-3-319-60645-3_29
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