NDLI: Nonlinear parameter prediction of fossil power plant based on OSC-KPLS

Content Provider	IEEE Xplore Digital Library
Author	Xi Zhang Shihe Chen Weiwu Yan Huihe Shao
Copyright Year	2011
Description	Author affiliation: Guangdong Electric Power Research Institute, China Southern Power Grid, No.73, Meihua Road, Guangzhou, 510600, China (Xi Zhang; Shihe Chen) \|\| Department of Automation, Shanghai Jiaotong University, No.800, Dongchuan Road, 200240, China (Weiwu Yan; Huihe Shao)
Abstract	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.
Starting Page	672
Ending Page	675
File Size	214895
Page Count	4
File Format	PDF
ISBN	9781457702686
e-ISBN	9781457702709
e-ISBN	9781457702693
DOI	10.1109/ICINFA.2011.5949078
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear Parameter estimation Laboratories orthogonal signal correction (OSC) Estimation Predictive models Data models Inferential control Kernel partial least squares (KPLS) Fossil power plant Kernel
Content Type	Text
Resource Type	Article

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