A machine learning strategy with restricted sliding windows for real-time assessment of accident conditions in nuclear power plants

Abstract

This paper suggests a machine learning strategy for accident data in nuclear power plants (NPPs) to assess accident conditions and provide informative real-time predictions for the emergency response organization (ERO) of NPP, which adopts time window based sub-models with a restricted sliding range. To verify the feasibility and effectiveness of the strategy, three problems are defined to be solved by machine learning models which predict break size, accident scenario, and core damage time (CDT) of loss of coolant accidents (LOCAs). To prepare learning and test data sets for sub-models, 10,000 LOCA event cases having various break sizes and accident scenarios were calculated and the results were transformed to hypothetical accident data as suitable for machine learning. The learning process of three models was analyzed in terms of the preparation of specific data sets used for learning and test, and the prediction errors of sub-models and their causes. The analysis of three models showed that the predicted break size, scenario, and CDT have sufficient accuracies and informative indications for supporting the ERO’s accident response decision making. For a better understanding of the prediction capabilities of the models and their applicability to real situations, an integrated prediction model (IPM) based on the combination of three models is designed and applied to tens of LOCA simulation cases whose characteristics like break sizes are chosen as suitable for sensitivity analysis purpose. The analysis of IPM prediction for simulation cases shows that overall prediction accuracy is very satisfactory and most errors in the prediction are caused by inherent difficulties in the specific accident condition itself. IPM’s prediction is reliable and understandable in most accident conditions. This concludes that the suggested learning strategy is highly applicable and very effective for the assessment of accident conditions in NPPs by machine learning.