Autonomous operation algorithm for safety systems of nuclear power plants by using long-short term memory and function-based hierarchical framework

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 115
  • Download : 0
With the improvement of computer performance and the emergence of cutting-edge artificial intelligence (Al) algorithms, an autonomous operation based on Al is being applied to many industries. An autonomous algorithm is a higher-level concept than conventional automatic operation in nuclear power plants (NPPs). In order to achieve autonomous operation, the autonomous algorithm needs to include superior functions to monitor, control and diagnose automated subsystems. This study suggests an autonomous operation algorithm for NPP safety systems using a function-based hierarchical framework (FHF) and a long short-term memory (LSTM). The FHF hierarchically models the safety goals, functions, systems, and components in the NPP. Then, the hierarchical structure is transformed into an LSTM network that is an evolutionary version of a recurrent neural network. This approach is applied to a reference NPP, a Westinghouse 930 MWe, three-loop pressurized water reactor. This LSTM network has been trained and validated using a compact nuclear simulator. (C) 2018 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2018-09
Language
English
Article Type
Article
Keywords

ARTIFICIAL NEURAL-NETWORKS

Citation

ANNALS OF NUCLEAR ENERGY, v.119, pp.287 - 299

ISSN
0306-4549
DOI
10.1016/j.anucene.2018.05.020
URI
http://hdl.handle.net/10203/244647
Appears in Collection
NE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0