Abstract
The goal of emergency operation in nuclear power plants (NPPs) is to ensure the integrity of reactor core as well as containment building under undesired initiating events. In this operation, operators perform the situation awareness, the confirmation of automatic actuation of safety systems, and the manual operations to cool down the reactor according to the operating procedures. This study aims to develop an autonomous operation agent that can reduce the pressure and temperature of primary system. The agent applies the Soft Actor-Critic (SAC) algorithm, which is a kind of deep reinforcement algorithm for optimizing stochastic actions. With the SAC, the agent is trained to find actions to meet the pressure and temperature curve criteria and the cooling rate. In addition, the test using a compact nuclear simulator demonstrates that the agent can cool down the reactor by manipulating the necessary systems in compliance with the constraints.
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Acknowledgments
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning under Grant N01190021-06, and in part by the Korean Government, Ministry of Science and ICT under Grant NRF-2018M2B2B1065651.
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Lee, D., Kim, J. (2021). Autonomous Emergency Operation of Nuclear Power Plant Using Deep Reinforcement Learning. In: Ahram, T.Z., Karwowski, W., Kalra, J. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-80624-8_65
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DOI: https://doi.org/10.1007/978-3-030-80624-8_65
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