Development of Autonomous Operation Agent for Normal and Emergency Situations in Nuclear Power Plants
Daeil Lee, Hyojin Kim, Younhee Choi, Jong‐Hyun Kim · 2021
Nuclear power plants (NPPs) are highly automated systems. Nevertheless, the operator’s manual actions are still required when normal (start-up/shutdown) operation and emergency operation. During these operations, operators execute necessary actions (e.g., situation awareness, confirmation of automatic actuation, and manipulation) following the operating procedures. This study suggests a Deep Reinforcement Learning (DRL)-based autonomous agent. The agent can manage the power increase operation from 2% to 100% and reduce the pressure and temperature until the shutdown cooling entry condition after reactor trip caused by loss of coolant accident in NPPs. The DRL-based controller suggested in this study combines a rule-based system and DRL algorithm that involves a Soft Actor-Critic algorithm and deep neural network. The test results using a compact nuclear simulator indicates that the agent can manipulate components to comply with identified constraints for start-up operation and emergency operation.