【作者】 王伟； 张航； 罗大庸；

【Author】 WANG Wei,ZHANG Hang,LUO Dayong School of Information Science and Engineering,Central South University,Changsha 410083,P.R.China

【机构】 中南大学信息科学与工程学院；

【摘要】 针对电厂烟气含氧量难以进行有效检测的问题,从提高模型在线自适应能力的角度出发,提出一种基于核模糊C均值聚类和局部建模方法的软测量模型。首先,采用核模糊C均值聚类算法对历史数据库进行聚类分析以形成若干子样本集;其次,判断当前时刻输入数据与各个聚类中心的相似度,在相似度最好的子样本集中进行遍历搜索,以获得建模邻域数据集;再次,采用基于多种群混合优化算法的最小二乘支持向量机方法建立烟气含氧量局部模型;最后,利用锅炉燃烧过程实际运行数据进行仿真研究。仿真实验表明,与标准最小二乘支持向量机建模方法的相比,本文算法具有更好的预测性能,虽然计算开销有所增加,但能够满足锅炉燃烧过程烟气含氧量检测的实时性要求。更多还原

【Abstract】 Based on the fact that the flue gas oxygen content in power plant is hard to detect effectively,a soft-sensing model based on kernel fuzzy C-means clustering and local modeling method is proposed from improving the online self-adaptive ability of the soft-sensing model.Firstly,several sub-sample sets are formed by using kernel fuzzy C-means clustering algorithm to cluster analysis of the history database.Secondly,the modeling neighborhood dataset is obtained through traversal search in the sub-sample set,whose clustering center has the highest similarity with the current input data.Thirdly,the least square support vector machine based on multi-population hybrid optimization algorithm is used to build the local model for flue gas oxygen content.Finally,the simulation experiments are carried out based on the actual operation data.Simulation results show that compared with the standard LSSVM soft-sensing model,although the computing cost is increased,the proposed soft-sensing model has better prediction performance and can satisfy the real-time requirements for flue gas oxygen content in boiler combustion process.更多还原