基于PSO-LSSVM模型的火电厂烟气含氧量预测  

Prediction of the Oxygen Content in Flue Gas of Power Plant based on PSO-LSSVM Model

作　　者：李建强[1] 张莹莹[1] 牛成林 LI Jian-qiang;ZHANG Ying-ying;NIU Cheng-lin(School of Energy Power and Mechanical Engineering,North China Electric Power University,Baoding,China,Post Code ： 071003)

机构地区：[1]华北电力大学能源动力与机械工程学院

出　　处：《热能动力工程》2018年第7期49-55,共7页Journal of Engineering for Thermal Energy and Power

摘　　要：锅炉烟气含氧量是机组运行最重要的参数之一,为了准确测量氧量,在支持向量机（SVM）的基础上,提出最小二乘支持向量机（LSSVM）,并结合粒子群算法（PSO）对模型参数（C,g）进行寻优,从而建立锅炉输入和输出变量之间的关系模型。将该方法应用到某电厂600 MW燃煤机组中,用训练后的模型进行预测,并与SVM模型预测结果进行比较。结果表明：采用LSSVM方法,能够辨识出多个变量与氧量之间的复杂关系,对锅炉氧量的预测误差为±0.03;并且PSO-LSSVM预测精度比PSO-SVM模型高,PSO-LSSVM模型具有预测精度高、泛化能力好、鲁棒性强和训练时间较短等优点。The oxygen content in flue gas of power plant is one of the most important parameters. In order to accurately predict the oxygen content, a prediction model was proposed for the boiler using Least Squares Support Vector Machine （LSSVM） and Particle Swarm Optimization （PSO） , with which the complex relation between input variables and output variable was successfully established. The proposed method was applied to a 600 MW pulverized coal-fired power plant. And the results of LSSVM were com- pared to those of SVM model. Results showed that the LSSVM method can recognize the complex relation- ship among many variables and the oxygen content, achieving the accurate prediction of the boiler oxygen content. The prediction accuracy of PSO-LSSVM is higher than that of PSO-SVM model. In summary, the PSO-LSSVM model has high prediction accuracy, and excellent generalization and stability, and requires short training time.

关 键 词：烟气含氧量 PSO LSSVM 鲁棒性 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置]