Yurdusev M.A.Ata R.Çetin N.S.2024-07-222024-07-22200603605442http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/19652Wind turbine blade design depends on several factors, such as turbine profile used, blade number, power factor, and tip speed ratio. The key to designing a wind turbine is to assess the optimal tip speed ratio (TSR). This will directly affect the power generated and, in turn, the effectiveness of the investment made. TSR is suggested to be taken between 7 and 8 and in practice generally taken as 7 for a 3-blade network-connected wind turbine. However, the optimal TSR is dependent upon the profile type used and the blade number and could fall out of the boundaries suggested. Therefore, it has to be assessed accordingly. In this study, the optimal TSR and the power factor of a wind turbine are predicted using artificial neural networks (ANN) based on the parameters involved for NACA 4415 and LS-1 profile types with 3 and 4 blades. The ANN structure built is found to be more successful than the conventional approach in estimating the TSR and power factor. © 2005 Elsevier Ltd. All rights reserved.EnglishAerodynamicsMachine designNeural networksOptimizationTurbomachine bladesWind powerWind turbinesAerodynamicsMachine designNeural networksOptimizationTurbomachine bladesWind powerBlade numberPower factorTip speed ratioTip speed ratio (TSR)artificial neural networkassessment methodwind turbineWind turbinesArticle10.1016/j.energy.2005.09.007