Simulation Modelling Practice and Theory, ( ISI ), Volume (17), No (7), Year (2009-5) , Pages (1290-1298)

Title : ( Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque )

Authors: Javad Sargolzaei , Ali Kianifar ,

Citation: BibTeX | EndNote

The power factor and torque of wind turbines are predicted using artificial neural networks (ANNs) based on experimental data which have been collected for seven prototype vertical Savonius rotors tested in a wind tunnel. In this research, the rotors with different configurations were located in the wind tunnel and the tests were repeated 4 to 6 times in order to reduce errors. Since the Reynolds number has a negligible effect on power ratio, therefore tip speed ratio (TSR) is the main input parameter to be predicted in neural network. Also, the rotor s power factor and torque were simulated for different tip speed ratios and different blade angles. The simulated results show a strong capability for providing reasonable predictions and estimations of the maximum power of rotors and maximizing the efficiency of Savonius turbines. According to artificial neural nets simulations and the experimental results, increasing tip speed ratio leads to a higher power ratio and torque. For all the tested rotors, a maximum and minimum amount of torque has happened at angle of 60o and 120o, respectively.

Keywords

, Neural networks, Savonius rotors, blade angles, TSR, Wind
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@article{paperid:1011450,
author = {Sargolzaei, Javad and Kianifar, Ali},
title = {Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque},
journal = {Simulation Modelling Practice and Theory},
year = {2009},
volume = {17},
number = {7},
month = {May},
issn = {1569-190X},
pages = {1290--1298},
numpages = {8},
keywords = {Neural networks; Savonius rotors; blade angles; TSR; Wind tunnel},
}

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%0 Journal Article
%T Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque
%A Sargolzaei, Javad
%A Kianifar, Ali
%J Simulation Modelling Practice and Theory
%@ 1569-190X
%D 2009

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