Proposal of a novel AI-based plant operator support system for the safety of nuclear power plants

Abstract

Enhancing the ability to manage abnormal situations is important for improvement of the safety of nuclear power plants. It is necessary to investigate potential risks thoroughly in advance, and prepare countermeasures against the identified risks. In addition, in case of an occurrence of an abnormal situation, plant operators are required to recognize the plant situation promptly and select a suitable countermeasure. However, the human ability to perform it is limited because the number of such abnormal situations in actual nuclear power plants is indefinite. Due to the advent of AI, it becomes possible to compensate for such limitation, by learning abnormal situations and assessing the effectiveness of prepared countermeasures virtually. The present study aims to develop such an AI-based support system for the plant operators to deal with abnormal situations steadily. Although many previous studies about detection of anomalies have been conducted, few studies consider countermeasures, especially against unexperienced abnormal situations. This study develops a novel plant operator support system designed not only to estimate details of anomalies in a plant but also propose countermeasures adaptively by employing several AI technologies of deep neural network and reinforcement learning. A plant simulator is used to prepare training data for the AI system. The combination of the proposed AI-based system and the plant simulator makes it possible to identify abnormal situations unknown to operators and propose countermeasures. The design and performance of the proposed system is illustrated using High Temperature engineering Test Reactor (HTTR) operated in Japan Atomic Energy Agency.

Keywords

• artificial intelligence
• deep neural network
• reinforcement learning
• surrogate model
• abnormal situation
• countermeasures