基于GRU-MLP的核动力装置运行监测数据异常检测与校正方法研究  

Research on Anomaly Detection and Correction of Nuclear Power Plant Operation Data Based on GRU-MLP

作　　者：王天舒 余刃[1] 毛伟[1] 宋霄森 马杰 Wang Tianshu;Yu Ren;Mao Wei;Song Xiaosen;Ma Jie(Naval University of Engineering,Wuhan,430033,China)

机构地区：[1]海军工程大学,武汉430033

出　　处：《核动力工程》2023年第5期188-194,共7页Nuclear Power Engineering

摘　　要：为改善核动力装置仪表与控制系统采集或存储的运行数据中出现的缺失、飘移和跳跃等数据质量问题,以便为运行数据分析和自动控制器提供更可靠的输入,提出了基于门控循环单元(GRU)与多层感知机(MLP)融合模型的核动力装置运行监测数据异常检测与校正的一体化方法。GRU-MLP融合模型提出了基于GRU模型的运行监测数据短时预测算法,为运行监测数据异常检测和校正提供参考依据,并且设计了实时校正机制,以提高GRU模型对包含异常运行数据的预测准确率。然后,利用MLP模型的非线性拟合能力优化“预测-异常检测”机制下使用的固定阈值为动态阈值,提高设计方法的异常检测准确率。最后,以某型核动力装置运行数据开展测试实验,从多角度分析并证明了所设计方法的准确性和可行性。In order to solve the data quality problems such as missing,drifting and jumping in the operation data collected or stored by the instrument control system of nuclear power plant and provide more reliable input for operational data analysis and automatic controllers,a hybrid model based on Gated Recurrent Unit and Multilayer Perception(GRU-MLP) is proposed to detect and correct the abnormal operation monitoring parameters data of nuclear power plant.Firstly,the shortterm prediction algorithm of operation data based on GRU model is studied to provide reference for anomaly detection and correction of operation data.Secondly,in order to improve the prediction accuracy of GRU model for the operation data containing anomalies,the real-time correction mechanism is used for optimization.Then,using the nonlinear fitting ability of MLP model,the fixed threshold used in the "prediction-anomaly detection" mechanism is optimized to the dynamic threshold,which improves the anomaly detection accuracy of the proposed method.Finally,the accuracy and feasibility of the proposed algorithm are verified through experiments based on the operation data of a certain nuclear power plant.

关 键 词：核动力装置 数据预测 数据异常检测与校正 门控循环单元(GRU) 多层感知机(MLP) 

分 类 号：TL382[核科学技术—核技术及应用]