Optimization and range expansion of IRIS reactor accident diagnosis model based on LSTM neural network

Small reactors have the advantages of compact structure and wide application scenarios, and can be deployed in remote areas. However, due to the complex structure and special application scenarios, it is more difficult for operators to diagnose and identify accident information. This article conducts accident diagnosis research using simulated data from IRIS reactors. An accident diagnosis model based on LSTM neural network has been established. The diagnostic ability of the model has been analyzed when there is an error in the time of the accident occurrence. When there are numerical and time errors in the dataset, the simulated diagnostic accuracy is 96.8 %. The method of using a diagnostic model based on shutdown signal partitioning has improved the diagnostic ability of transient operating events and minor accident conditions, achieving an accuracy of 96.1 % when there is ±5 % noise in the dataset. By optimizing the output form of the diagnostic model, the diagnosis of unknown accidents was achieved. When using LOCA and LOFA accidents as unknown accidents, the diagnostic accuracy of the model reached 98.5 %.