基于SSA-LSTM的海洋条件下稳压器液位回归预测研究  

Research on Regression Prediction of Pressurizer Liquid Level under Ocean Conditions Based on SSA-LSTM Neural Network

作　　者：李东阳 权紫轩 张彪 李江宽 谭思超 田瑞峰 Li Dongyang;Quan Zixuan;Zhang Biao;Li Jiangkuan;Tan Sichao;Tian Ruifeng(Heilongjiang Provincial Key Laboratory of Nuclear Power System&Equipment,Harbin Engineering University,Harbin,150001,China;Key Laboratory of Nuclear Safety and Advanced Nuclear Energy Technology,Ministry of Industry and Information Technology,Harbin Engineering University,Harbin,150001,China;Wuhan Second Ship Design and Research Institute,Wuhan,430062,China)

机构地区：[1]哈尔滨工程大学黑龙江省核动力装置性能与设备重点实验室,哈尔滨150001 [2]哈尔滨工程大学核安全与先进核能技术工信部重点实验室,哈尔滨150001 [3]武汉第二船舶设计研究所,武汉430062

出　　处：《核动力工程》2025年第2期293-299,共7页Nuclear Power Engineering

摘　　要：为保证核动力装置在海洋环境下的安全运行,有必要建立一套计算模型获得稳压器内的实时液位。通过搭建实验系统采集相关数据,采用基于麻雀搜索算法(SSA)优化长短期记忆(LSTM)神经网络(SSA-LSTM),根据测得的压力、运动姿态等关键参数建立液位回归预测模型。研究结果表明,所建立的液位回归预测模型预测精度优秀,明显优于其他传统神经网络。此外,该模型的泛化能力良好,对于新鲜样本的预测精度也较高,将其集成到控制系统中可实时输出稳压器液位,从而提高海洋条件下核动力装置运行的安全性,并为后续核动力装置的智能运维提供参考。To ensure the safe operation of the nuclear reactor system in the ocean environment,it is necessary to establish a set of computational models to obtain the real-time liquid level in the pressurizer.By building an experimental system to collect relevant data,the Long-Short Term Memory(LSTM)neural network is optimized based on the sparrow search algorithm(SSA),and the liquid level regression prediction model is established according to the measured key parameters such as pressure and motion attitude.The research results show that the prediction accuracy of the established liquid level regression prediction model is excellent,which is obviously better than other traditional neural networks.The model has good generalization ability,and the prediction accuracy of fresh samples is still acceptable.By integrating the model into the control system,the liquid level can be output in real time,which can improve the safety of nuclear power operation under ocean conditions and provide reference for the intelligent operation and maintenance of nuclear power.

关 键 词：海洋条件 麻雀搜索算法(SSA) 长短期记忆(LSTM)神经网络 液位回归预测 

分 类 号：TL334[核科学技术—核技术及应用]