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Sensorless Estimation of Wind Speed by Soft Computing Methodologies: A Comparative Study
Volume 26, Issue 3 (2015), pp. 493–508
Dalibor Petković   Muhammad Arif   Shahaboddin Shamshirband   Ehab Hussein Bani-Hani   Davood Kiakojoori  

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https://doi.org/10.15388/Informatica.2015.60
Pub. online: 1 January 2015      Type: Research Article     

Received
1 November 2013
Accepted
1 January 2015
Published
1 January 2015

Abstract

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The results are compared with the adaptive neuro-fuzzy (ANFIS) results.

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Vilnius University

Keywords
wind pace wind turbine ANFIS support vector regression soft computing

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INFORMATICA

  • Online ISSN: 1822-8844
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