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吉川 雅紀; 関 暁之; 沖田 将一朗; 高屋 茂; Yan, X.
no journal, ,
Downsizing number of operators for advanced nuclear power plant is required in terms of economic performance. However, there is a lack of experience in operating advanced nuclear power plant. Therefore, it is important to develop a support system to make plant state normal if anomalies ocured. To meet the demand, we develop 
, which, from measured plant values, proposes implementation plans on control apparatuses to recover state of plant. We adopt reinforcement learning to develop this system. By using reinforcement learning, it is expected that the system can deal with broader scope of anomalies than that of followed by conventional human review. In this paper, we present basic concept of the system and show the efficiency of it under some assumptions.
吉川 雅紀; 関 暁之; 沖田 将一朗; 高屋 茂; Yan, X.
no journal, ,
Sufficient experience and skilled personnel are required in operation of NPPs, but these are lacking in advanced NPPs. Therefore, there is a need for support systems to help operators deal with anomalies in advanced NPPs. We are developing such a system, which we call Countermeasure Proposal System, by using reinforcement learning models. By adopting reinforcement learning models, it is expected to automatically and immediately propose countermeasures against various abnormalities in advanced NPPs. In this paper, to improve efficiency of Countermeasure Proposal System, we conducted grid searching for three hyperparameters in PPO. As the result of the survey, we found pairs of hyperparamters, which provides the most effective Countermeasure Proposal System.