Transient trend prediction of safety parameters for small modular reactor considering equipment degradation

Abstract

Small modular reactor (SMR) has strong coupling characteristics and complex nonlinearities. Predicting the trend of SMR safety parameters and monitoring whether the parameter values exceed operational limits and conditions (OLC) before performing a transient helps the operator to judge the safety of the transient, especially in the case of equipment degradation. In this paper, a transient trend prediction method of SMR safety parameters based on random forest model was proposed, which considered the impact of equipment degradation on safety parameters. The transient data used for model training included different load-following conditions considering various degraded degrees of rotating speed drop in the main coolant pump (MCP) or effective heat transfer area reduction in the once-through steam generator (OTSG). To evaluate the model, the new untrained transient data was used as test data, the results showed that safety parameters were more accurately predicted by random forest, compared with the backpropagation neural network.