摘要
为了解决核电站冷源系统海洋生物入侵问题,建立海洋生物探测预警模型,以大亚湾核电站为研究对象,从影响核电厂海洋生物入侵主要原因出发,分析了海洋生物密度、相对流速、相对风速、盐度和温度5个因素对海洋生物入侵的影响。结果表明:基于线性回归模型和模糊神经网络两种预测方法分别对多传感器的采样数据进行分析计算,得到了基于多源信息融合的海洋生物探测预警模型;通过对5种因素数据特征的融合技术,即可得到海生物入侵强度值,进而判断核电厂取水口堵塞的可能性;实测数据表明,两种算法的估计误差均可控制在[-0.2,0.2]以内,欧氏距离分别0.0168、0.0078。研究表明,本研究中得到的海洋生物探测预警模型预测精度高,可用于核电站海洋生物的探测预警,保证核电冷源安全。
An early warning model of marine organism detection is established to deal with the problem of marine organism invasion in a nuclear power plant cold source system based on multi-source information fusion.The effects of marine biological density,relative current velocity,relative wind speed,salinity and temperature on marine organism invasion were evaluated and influence coefficients and influence functions of five factors were calculated to establish the invasion organism detection model.Based on linear regression model and fuzzy neural network prediction method,the sampling data of multi-sensor were analyzed and calculated,and the early model of marine organism invasion was obtained.The results showed that the value of marine biological invasion intensity was obtained,and then the possibility of water intake blockage in nuclear power plant was judged by fusing the data characteristics of five factors.The measured data showed that the estimated error percentage of the two algorithms were within[-0.2,0.2],and the Euclidean distances were 0.0168 and 0.0078,respectively.The proposed marine organism detection and early warning model here is characterized by high precision,detection and early warning of marine organism in nuclear power plants to ensure the safety operation in the nuclear power cold source system.
作者
孟威
刘杨
李建文
刘笑麟
郭显久
成志娟
MENG Wei;LIU Yang;LI Jian-wen;LIU Xiao-lin;GUO Xian-jiu;CHENG Zhi-juan(Suzhou Nuclear Power Research Institute Company Limited,Suzhou 215004,China;College of Information Science and Technology,Dalian Maritime University,Dalian 116026,China;College of Electronic and Information Engineering,Dalian Jiaotong University,Dalian 116028,China;College of Information Engineering,Dalian Ocean University,Dalian 116023,China)
出处
《大连海洋大学学报》
CAS
CSCD
北大核心
2019年第6期840-845,共6页
Journal of Dalian Ocean University
基金
国家重点研发计划项目(2017YFC1404400,第4、6课题)
中广核尖峰计划项目(R-2016SZRE31TF)
国家自然科学基金资助项目(61503054)
关键词
海洋生物
预警模型
多传感器
入侵强度
信息融合
marine organism
early warning model
multi-sensor
invasion intensity
information fusion
