Nonlinear parameter prediction and estimation of fossil power plant based on kernel partial least squares

In this paper, a novel parameter prediction and estimation method of fossil power plant based on kernel partial least square is proposed. The proposed method can effectively capture the nonlinear relationship among process variables and has better estimation performance than PLS and other linear approaches. Simulation results of some 1000MW power plant´s data set prove that the method is effective. Its performance significantly outperforms estimation method based on PLS and PCR.