Report for 2.2.1 Task 5: Develop and Document a State-Based Alarm System for a Nuclear Power Plant Control Room Using Machine Learning. Light Water Reactor Sustainability Program report

Technical Report · Thu Aug 29 04:00:00 EDT 2019

DOI:https://doi.org/10.2172/1560801· OSTI ID:1560801

Langstrand, Jens-Patrick ^[1]; Nguyen, Hoa ^[1]; McDonald, Robert ^[1]

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1. Inst. for Energy Technology, Kjeller (Norway). Halden Reactor Project

In legacy control rooms the operators might have 20 to 30 alarms to deal with on 12 control boards, during a reactor trip. The new digital alarm system in the upgraded control room will display between 150 and 200 alarm points on a single screen. This overload of information prevents the operators from identifying any abnormal alarms quickly without having to scroll through the alarm list looking for the abnormal alarms. One way to tackle this problem could be to attempt to create a state-based alarm system which would identify plant states and would identify which alarms are expected in those states and suppress them from the alarm screen so that any unexpected alarms would be displayed and easily identified to the operators. This can be done today but is very labor intensive in that an operator or training instructor would be required to run scenarios and log the alarms to be suppressed. The hope is that Machine Learning (ML) can provide an alternative solution with less manual effort required.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE)
• DOE Contract Number: AC07-05ID14517
• OSTI ID: 1560801
• Report Number(s): INL/EXT--19-55368-Rev000; IFE/INL-196543
• Country of Publication: United States
• Language: English

Related Subjects

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS
Alarm Management
Alarm Systems
Control Room
Human Factors
Machine Learning
Nuclear Power Plant