Sensorless Estimation of Wind Speed by Soft Computing Methodologies: A Comparative Study

doi:10.15388/Informatica.2015.60

Sensorless Estimation of Wind Speed by Soft Computing Methodologies: A Comparative Study

2015-01-01

6

PRE

AI

Shahaboddin Shamshirband *

D

Davood Kiakojoori

DOI：10.15388/Informatica.2015.60

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

wind pace

wind turbine

ANFIS

support vector regression

soft computing

Sensorless Estimation of Wind Speed by Soft Computing Methodologies: A Comparative Study

Abstract

Journal

Organization