Research on Intelligent Monitoring and Warning Algorithms for Unexpected Reactor Shutdown Events in Nuclear Power Plants

During normal operation of nuclear power plants, unintended protection actions, such as stopping the reactor, shutdown, load shedding, etc., may occur due to equipment failure, instrumentation and control system failure, and human error. The discovery of abnormal conditions during current unit operation mainly relies on the threshold alarm information of the DCS system and lacks the analysis of trends. The study establishes the logical relationship between variables through event logic, and based on this uses auto-associative neural network (AANN) modeling to detect anomalies in associated variables, and finally uses empirical modal decomposition (EMD) trend extraction algorithm with adaptive sliding window Holton linear trend model fitting to predict anomalous variables. It is able to realize the advance and accurate warning of stopping reactor shutdown events, so that the operation and maintenance personnel can find and solve the problems earlier and improve the safety of nuclear power operation. The article utilizes the simulation data and the real abnormal data of the unit to conduct test experiments, and obtains the real data experimental results with a mean square error of 0.1 and an R2 of 0.99, and at least more than one hour in advance of the shutdown action for early warning, which verifies the accuracy of the methodology and the ability of early warning.