An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines
| dc.contributor.author | Ata, R | |
| dc.contributor.author | Kocyigit, Y | |
| dc.date.accessioned | 2024-07-18T11:39:50Z | |
| dc.date.available | 2024-07-18T11:39:50Z | |
| dc.description.abstract | This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model to predict the tip speed ratio (TSR) and the power factor of a wind turbine. This model is based on the parameters for LS-1 and NACA4415 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the TSR and power factor were taken as output variables. After a successful learning and training process, the proposed model produced reasonable mean errors. The results indicate that the errors of ANFIS models in predicting TSR and power factor are less than those of the ANN method. (C) 2010 Elsevier Ltd. All rights reserved. | |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.other | 1873-6793 | |
| dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/1941 | |
| dc.language.iso | English | |
| dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | |
| dc.title | An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines | |
| dc.type | Article |