基于多主体系统架构的锅炉汽包应力动态软测量  

Dynamic Soft Sensor of Boiler Drum Stress Based on Multi-agent System Frame

作　　者：李军[1] 王雷[1] 洪军[1] 徐治皋[1] 

机构地区：[1]东南大学动力工程系,江苏省南京市210096

出　　处：《中国电机工程学报》2008年第2期118-122,共5页Proceedings of the CSEE

摘　　要：实时监测运行时汽包的应力变化情况,是电厂安全管理的重要内容。传统方法在进行汽包应力的计算时,做了较多的简化,计算结果的准确度不能得到保证。有限元计算方法能保证计算结果的准确度,但是计算量过大,不能满足实时计算的要求,所以只能用于离线的优化设计工作。文中利用神经网络优越的非线性逼近能力,在汽包应力与汽包金属壁温序列及汽包压力之间建立一种映射关系,实现了汽包应力的软测量。实际运行中,汽包内蒸汽压力和温度的变化范围较大,需要引入多Agent系统设计原理,将较大的神经网络结构分解成一组结构较小的子神经网络,因此每个子神经网络的结构小巧,学习和泛化性能好。该模型能根据不同输入汽包压力和汽包温度,经感知模块判别后,由不同的子神经网络做出响应,节约了系统资源,提高了响应速度。经过仿真验证,计算精度达到工程要求,可以实际应用。On-line monitoring the stress of the boiler drum, is a very important content of power plant safety management. For traditional methods do many simplifications in computing drum stress, accuracy of the results can not be ensured. Finite element method can make fine accuracy, but it can not be used in on-line Computing for its large computational quantities. So finite element method is only used in off-line optimal design. The nonlinearity of neural network is used in constructing the mapping relation between drum stress and temperature series of drum wall and pressure of drum. For pressure and temperature of drum vapor vary in a wide range, multi-agent system（MAS） design method was introduced. A big neural network can be divided into many small sub neural networks by this method, so the structure of sub neural network is smart and has good learning and generalization performance. Model of MAS can decide which sub neural network to response for the inputs, according to the pressure and temperature of the drum via classification of the perception part, therefore resource of system is spared and response speed is enhanced. By simulation, the accuracy of results can meet the industrial demand and this model can be used in practice.

关 键 词：神经网络 时间序列 锅炉汽包 应力 多主体系统 软测量 

分 类 号：TK227[动力工程及工程热物理—动力机械及工程]