Nonlinear Parameter Prediction of Fossil Power Plant Based on OSC-KPLS
In order to solve problems of the failure of measured parameters and realize online optimal running in fossil power plant,a novel parameter prediction and estimation method based on orthogonal signal correction (OSC)and kernel partial least squares (KPLS)is proposed.OSC is a data preprocessing method that remove from X information not correlated to Y.Kernel partial least square is a promising regression method for tackling nonlinear problems because it can efficiently compute regression coefficients in high-dimensional feature space by means of nonlinear kernel function.In this paper,the prediction performance of the proposed approach (OSC-KPLS)is compared to those of PLS,OSC-PLS and KPLS using industrial example.OSC-KPLS effectively simplifies both the structure and interpretation of the resulting regression model and shows superior prediction performa-nce compared to PLS,OSC-PLS and KPLS.
Xi Zhang Shihe Chen Weiwu Yan Huihe Shao
Guangdong Electric Power Research Institute China Southern Power Grid No.73,Meihua Road,Guangzhou,51 Department of Automation Shanghai Jiaotong University No.800,Dongchuan Road,Shanghai,200240,China
国际会议
深圳
英文
672-675
2011-06-06（万方平台首次上网日期，不代表论文的发表时间）