Abstract
In order to reduce operator workload from startup and shutdown operations for existing Nuclear Power Plants (NPPs), it is necessary to develop an automation system based on deep learning, the leading approach in current Artificial Intelligence (AI) technology. From existing research, it is challenging to develop an automation system using conventional machine learning for startup and shutdown operation since the automation system needs to be able to handle many instances of both monitoring and control variables in NPPs. Deep learning is able to simulate a variety of operating actions based on the experience of each operator. In this study, an AI framework for an automation system for startup operation in NPPs has been developed using a Recurrent Neural Network (RNN), which is a robust deep learning method for time series analysis. A feasibility study for an AI framework for the automation system is conducted using a Compact Nuclear Simulator (CNS) based on Westinghouse three-loop NPPs. The target scenario for the feasibility study is operation under bubble creation conditions in a pressurizer under startup.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Institute of Control, Robotics and Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
