核动力装置运行数据的特征提取方法研究

Research on Feature Extraction Method for Operating Data of Nuclear Power Plant

随着传感器技术的发展,核动力装置能采集和监测的运行参数越来越多,这不仅加大了操纵员的负担,而且提升了监测系统的负载。考虑到大多数参数之间具有相关性且部分参数是冗余参数,其中的有效信息可用少数参数表达,因此提出了运用机器学习方法稀疏自动编码器对核动力装置的运行参数进行特征提取,然后将提取的特征数据应用到状态监测中。结果表明,在测试样本数据中分别包含单一正常工况数据和多种正常工况数据情况下,经过特征提取后的数据不仅能提升状态监测的精度,而且还能减少计算资源,这对提升核动力装置的安全性具有重要的指导意义。

Compared with the past,there are more important parameters available for collection and monitoring during the operation of nuclear power plant due to the deve-lopment of sensor technology.This situation not only increases the task of the operator,but also increases the load on the monitoring system.Since most parameters are correlated and some are redundant,the effective information in the parameters can be represented by a few parameters.In response to the above premise,the sparse autoencoder was used in this paper to extract the features of operating parameters of nuclear power plant.These feature data were then used in condition monitoring.The results show that using the data obtained by feature extraction for condition monitoring can not only improve the accuracy of state monitoring,but also reduce the computing resource,and this conclusion is applicable to both single and multiple normal working conditions.The results have important guiding significance for improving the safety of nuclear power plant.