Paper Title:
Application of Status Monitoring of Wind Turbines Based on Relevance Vector Machine Regression
  Abstract

Based on the single kernel function relevance vector machine(RVM) models,a multiple load-forecasting model has been established and simulated with several compound kernel functions, including Gauss kernel, Laplace, linear compounded by Gauss and Laplace, Gauss and polynomial kernel. Each model gained comparatively reasonable results in simulation .Moreover, multi linear-compound kernel RVMs performed better than single kernel RVMs in terms of most evaluating indicators, which prove that RVM is an appropriate machine learning method in monitoring status of components of wind turbines.

  Info
Periodical
Advanced Materials Research (Volumes 347-353)
Chapter
Chapter 3: Development and Utilization of Wind Energy
Edited by
Weiguo Pan, Jianxing Ren and Yongguang Li
Pages
2337-2341
DOI
10.4028/www.scientific.net/AMR.347-353.2337
Citation
J. P. Sun, L. T. Hu, "Application of Status Monitoring of Wind Turbines Based on Relevance Vector Machine Regression", Advanced Materials Research, Vols. 347-353, pp. 2337-2341, 2012
Online since
October 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Miao Chao Chen, Yu Zhao
Chapter 6: Computational and Computer Science Technology, Simulation, Modeling, Algorithms
Abstract:In this paper, we investigate the parameters estimation in nonlinear regression models. Firstly, a Gauss-Newton iteration method is given to...
571