Research on Intelligent Accident Diagnosis Model of Nuclear Reactor Coolant System

The nuclear reactor coolant system (NRCS) is one of the most critical systems in nuclear power plants, making the implementation of effective accident diagnosis highly significant. Although artificial intelligence technology has been extensively employed in the field of accident diagnosis for nuclear power plants, conventional models often suffer from shortcomings such as insufficient accuracy and poor generalizability, which fail to meet the stringent requirements for accident diagnosis of the NRCS. To address these issues, this study establishes a new intelligent accident diagnosis model for NRCS. Firstly, to enhance the accuracy of accident diagnosis, an NRCS accident diagnosis model (CNN-GRU) integrating convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed; Firstly, to enhance the diagnostic accuracy of the model, convolutional neural networks (CNN) and gated recurrent unit (GRU) were integrated. The powerful feature extraction capabilities of CNN and the efficient time-series data classification abilities of GRU were combined to establish the NRCS accident diagnosis model (CNN-GRU). Secondly, to enhance the generalizability of the model, the grey wolf optimizer (GWO) algorithm was used to adaptively optimize the hyperparameters within the CNN-GRU model, thereby establishing the NRCS intelligent accident diagnosis model (GWO-CNN-GRU). Finally, to validate the performance of the proposed model, the NRCS in PCTRAN were used as research subjects, simulating the diagnostic process of one normal operating condition and four typical accident conditions. The results demonstrated that the proposed model achieved an average accident diagnosis accuracy of 99.6% on the NRCS test set for the CPR1000 reactor type, which is an improvement of 2.1% and 1.5% compared to the GRU and CNN-GRU models, respectively; Similarly, on the NRCS test set for the AP1000 reactor type, the proposed model achieved an average accident diagnosis accuracy of 99.5%, representing an increase of 1.7% and 1.3% over the other two models, respectively. Therefore, the model proposed in this paper demonstrates superior performance in terms of accuracy and generalizability, providing a valuable reference value for intelligent accident diagnosis of the NRCS.