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 performance compared to PLS, OSC-PLS and KPLS.