Affiliations: [1] University of Niš, Faculty of Mechanical Engineering, Department for Mechatronics and Control, Aleksandra Medvedeva 14, 18000 Niš, Serbia | [2] Department of Computer Science, Gabriel College Mandi Bahauddin, Pakistan | [3] Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia | [4] Department of Mechanical Engineering, School of Engineering, Australian College of Kuwait, Kuwait | [5] Islamic Azad University of Chalus, Iran, e-mail: [email protected]
Correspondence:
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Corresponding author.
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.
