Research on the prediction of LOCA condition in nuclear power plants based on GRU recurrent neural network and its variants

Considering the development of deep learning and the emergence of intelligent control demands in nuclear reactors, along with the presence of plant-level real-time information monitoring systems in most nuclear power plants, there is a considerable accumulation of sensor measurements from long-term operation. This makes it feasible to conduct medium to long-term predictions for various real-time conditions in nuclear power plants. Therefore, this paper proposes the utilization of a gate-based recurrent neural network called GRU (Gated Recurrent Unit) and its variants for parameter prediction of LOCA (Loss of Coolant Accident) scenarios. The main content of this paper consists of two parts: (1) Experimental verification is conducted to demonstrate that GRU has excellent capability in capturing long-term sequential information and generalization ability, making it suitable for predicting accident conditions in nuclear power plants. Two accident trend prediction methods based on the GRU network are proposed for scenarios with limited data. The results show that these methods can effectively provide short-term development trends for accident conditions. Additionally, by considering the feature extraction capacity of CNN, the fusion of CNN and GRU models is employed for parameter prediction under different sizes of broken area. The results indicate an improvement in the model's generalization ability. (2) In scenarios with limited and incomplete data, a more robust variant of GRU called GRU-D model is utilized for both univariate and multivariate synchronous prediction of accident conditions with different missing values. Experimental results demonstrate that even with a data missing rate of 90%, the GRU-D network exhibits excellent predictive accuracy and generalization ability in parameter prediction for the given conditions.