基于灰色关联分析法及GSA-LSSVM的汽轮机排汽焓预测模型  

Prediction Model of Steam Turbine Exhaust Enthalpy Based on Grey Correlation Analysis Method and GSA-LSSVM

作　　者：王惠杰[1] 范志愿 许小刚[1] 李鑫鑫[1] 

机构地区：[1]华北电力大学能源动力与机械工程学院,河北省保定市071003

出　　处：《电力建设》2016年第11期115-122,共8页Electric Power Construction

基　　金：中央高校基本科研业务费专项资金资助项目(12NQ40);北京市自然科学基金项目(3132028)~~

摘　　要：排汽焓是汽轮发电机组热经济性诊断必不可少的一个参数。通过汽轮机功率方程与灰色关联分析(grey correlation analysis,GCA)理论确定了模型的输入变量,利用万有引力搜索算法(gravitational search algorithm,GSA)优化了最小二乘支持向量机(least squares support vector machine,LSSVM)的惩罚因子μ以及核径向范围σ2个参数。通过比较分析,选用RBF_kernel为LSSVM的核函数。以GCA-GSA-LSSVM为基础,建立了预测汽轮机排汽焓的数学模型,并将其与BP神经网络、RBF神经网络进行对比,同时分析了该数学模型的鲁棒性。结果表明基于GCA-GSA-LSSVM的汽轮机排汽焓预测模型具有精度高、泛化能力强、鲁棒性强等优点,该方法为精确预测机组节能潜力提供了一种有力的工具。The steam exhaust enthalpy is an essential parameter for the thermal economic diagnosis of steam turbine generator group. We determine the input variables of the model by steam turbine power equation and grey correlation analysis（ GCA） method,and optimize the punishment factor μ and nuclear radial range σ of least square support vector machine（ LSSVM） by gravitation search algorithm（ GSA）. The RBFkernel is selected as the kernel function of LSSVM through the comparative analysis. Based on the GCA-GSA-LSSVM,this paper establishes the mathematical model to predict the exhaust enthalpy of steam turbine,compares it with the BP neural network and RBF neural network,and analyzes its robustness. The results show that the prediction model of steam turbine exhaust enthalpy based on GCA-GSA-LSSVM has the advantages of high precision,strong generalization ability and strong robustness. This method provides a powerful tool for accurately predicting the energy saving potential of the unit.

关 键 词：最小二乘支持向量机(LSSVM) 万有引力搜索算法(GSA) 灰色关联分析法(GCA) 汽轮机排汽焓 

分 类 号：TM621[电气工程—电力系统及自动化]