基于卷积长短期记忆网络和人工鲸鱼算法的核反应堆运行事件诊断方法研究

当核电厂发生异常后应及时诊断原因，以避免对运行人员和周围环境造成严重后果。本文利用卷积神经网络（CNN）和长短期记忆（LSTM）网络可更好地提取数据的局部特征和记忆时间序列信息的特征，研究基于卷积长短期记忆（CLSTM）网络和人工鲸鱼算法的核反应堆运行事件诊断技术。通过核电厂反应堆模拟机仿真实验对本文所述方法进行测试，最终测试准确率为99.91%，证明了本文所述研究方法的有效性。相关研究成果可作为核电厂运行事件的一种诊断方法，有利于提高运行事件诊断的智能化和信息化水平，为核电厂的少人值守甚至无人值守提供技术基础，提高公众对核电厂的认识与信赖。 

Research on Diagnosis Method of Operational Events of Nuclear Reactor Based on Convolutional Long Short-term Memory Network and Artificial Whale Algorithm

In case of abnormality in the nuclear power plant, the cause shall be diagnosed in time to avoid serious consequences to the operators and the surrounding environment. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) network can better extract the local characteristics of data and the characteristics of memory time series information, and study the operational event diagnosis technology of nuclear reactor based on convolutional long short-term memory (CLSTM) network and artificial whale algorithm. The method described in this paper was tested by the simulation experiment of nuclear power plant reactor simulator, and the final test accuracy is 99.91%, which proves the effectiveness of the research method described in this paper. The relevant research results can be used as a diagnosis method of nuclear power plant operational events, which is conducive to improving the intelligence and information level of operational event diagnosis, providing a technical basis for few or no people on duty in nuclear power plants, and improving the public's understanding and trust in nuclear power plants.