Browsing by Author "Çetin N.S."

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    Assessment of optimum tip speed ratio of wind turbines
    (Association for Scientific Research, 2005) Çetin N.S.; Yurdusev M.A.; Ata R.; Özdemir A.
    The first thing to do in wind turbine blade design is to select tip speed ratio. Generally speaking, the speed ratio depends on the profile type used and the number of blades. Various speed ratios could be chosen for different types of profiles with different number of blades. Therefore, an optimization procedure should be applied to find the best ratio since this directly affects the energy generated from the turbine and in turn the investment made. This study presents a procedure to assess the optimum speed ratios for various profile types used in practice with various numbers of blades.
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    Assessment of optimum tip speed ratio in wind turbines using artificial neural networks
    (Elsevier Ltd, 2006) Yurdusev M.A.; Ata R.; Çetin N.S.
    [No abstract available]
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    Assessment of optimum tip speed ratio in wind turbines using artificial neural networks
    (Elsevier Ltd, 2006) Yurdusev M.A.; Ata R.; Çetin N.S.
    Wind 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.
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    Construction and energy generation of 3 kW autonomous wind turbine; [3 kW otonom bi̇r rüzgar türbi̇ni̇ kurulumu ve enerji̇ eldesi̇]
    (2008) Raşit A.; Çetin N.S.
    The autonomous wind turbine which has 3 kWh energy capacity has been installed Kirkaǧaç MYO (Celal Bayar University) in Kirkaǧaç. The established wind turbine is variable speed, three blades and its towers height is 15m. Electrical energy conversion is supplied via permanent-magnetic synchronous machine. The established system is operated as half of automatic via control component. Cp-λ curve has been drawn via required measurements obtained from turbine. At last, annual energy output values are calculated for wind turbine.
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    Analysis of height affect on average wind speed by ann
    (Association for Scientific Research, 2011) Ata R.; Çetin N.S.
    The power generated by wind turbines depends on several factors. Two of them are the wind speed and the tower height of wind turbine. In this study, the annual average wind speed based on the tower height is predicted using Artificial Neural Networks (ANN) and comparisons made with conventional model approach. The backpropagation multi layer ANNs were used to estimate annual average wind speed for three locations in Turkey. The Model has been developed with the help of neural network methodology. It involves four input variables-wind speed of measured location, desired height on measured location, height above ground level of measured location and Hellmann coefficient and one output variables-annual average wind speed. The model accuracy is evaluated by comparing the conventional model results with the actual measured and calculated values. © Association for Scientific Research.