This paper describes the issues ofthe high pe brmance time − ef − flight ultrasoriic flow 』 rneter applied to the reactor feedwater measurement system of nuclear power plants . The reactor thermal power is determined by a ca ] orimetric heat balance caiculation based on several parameters obtaineCl froin specific
Presenting important alarms selected from a large number of activated alarms provides useful operational support under a transient status in a nuclear power plant. We have developed an alarm processing method which selects and presents important alarms depending on plant status.In this method, important alarms are selected, first, based on physical relationships between alarms and component status including alarms themselves and second, even more important alarms are selected from the previously selected alarms according to the identified initial event causing the transient. Identification of the initial event is implemented by a neural network. The identified initial event and selected important alarms are presented to show the cause and influence of the transient.A prototype based on the above alarm processing method was validated during the start-up test at Kashiwazaki Kariwa Nuclear Power Plant Unit Number 4 of Tokyo Electric Power Co. The initial events, which were load rejection, turbine trip and main steam isolation valve closure, were correctly identified and about 30% of all activated alarms were selected as important. It was verified by an operating expert that the presentation of the identified initial event and the selected important alarms were effective to understand rapidly and correctly the transient status of the plant.
Presenting important alarms selected from a large number of activated alarms provides useful operational support under a transient status in a nuclear power plant. We have developed an alarm processing method which selects and presents important alarms depending on plant status.In this method, important alarms are selected, first, based on physical relationships between alarms and component status including alarms themselves and second, even more important alarms are selected from the previously selected alarms according to the identified initial event causing the transient. Identification of the initial event is implemented by a neural network. The identified initial event and selected important alarms are presented to show the cause and influence of the transient.A prototype based on the above alarm processing method was validated during the start-up test at Kashiwazaki Kariwa Nuclear Power Plant Unit Number 4 of Tokyo Electric Power Co. The initial events, which were load rejection, turbine trip and main steam isolation valve closure, were correctly identified and about 30% of all activated alarms were selected as important. It was verified by an operating expert that the presentation of the identified initial event and the selected important alarms were effective to understand rapidly and correctly the transient status of the plant.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.