摘要
以华能嘉祥电厂330 MW机组为例,针对火电厂飞灰含碳量影响因素的非线性和耦合性的问题,提出一种优化样本的KPCA(核主元分析)方法,利用加权相似度优化样本,再对其进行核主元分析,建立基于加权相似度的KPCA和LSSVM(最小二乘支持向量机)的炉膛温度软测量模型。现场实测数据表明,运用该软测量模型监测效果良好,不仅避免了在测量装置损坏时影响生产的弊端,满足飞灰含碳量实时测量要求,还为提高锅炉运行效率提供了依据。
Taking the 330 MW unit of Huaneng Jiaxiang power plant as an example,the KPCA （kernel principal component analysis） method is proposed to analyze impacting factors on the carbon content of the fly ash in the power plant.The KPCA and LSSVM （least square support vector maehine）are used to establish the model of the furnace temperature.Field test data show that the use of the soft measurement model is of considerable resuh,whieh helps to avoid the negative affeet to the production when any damage occurred to the measuring devices and meets the requirements of real-time measurement of carbon content in fly ash.More over,it also helps to improve the effieiency of the boiler.
出处
《山东电力技术》
2015年第10期40-43,50,共5页
Shandong Electric Power
关键词
飞灰含碳量
加权相似度
核主元分析
最小二乘支持向量机
软测量
carbon content of fly ash
weighted similarity
kernerl prineipal component analYsis
least squares support veetor maehine
soft measurement
