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Artificial Neural Networks for Predicting the Electrical Power of a New Configuration of Savonius Rotor

  • Youssef KassemEmail author
  • Hüseyin Gökçekuş
  • Hüseyin Çamur
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1095)

Abstract

The electrical power of new configuration Savonius turbine is estimated using multilayer perceptron neural network (MLPNN)‬ and radial basis function neural network (RBFNN) based on the experimental data which have been collected for 108 vertical axis wind turbine. In this work, the experiments were conducted at air velocities ranging from 3 to 12 m/s in front of a low-speed subsonic wind tunnel. Based on the experimental results, the new configuration of Savonius resulted in a noticeable improvement in the power compared to that of the classical Savonius turbine. This study reveals that the MLPNN has the potential of predicting the mechanical power of a Savonius wind turbine with minimal prediction error scores compared to the RBFNN model.

Keywords

Electrical power Savonius turbine MLPNN RBFNN 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Civil and Environmental EngineeringNear East UniversityNicosiaTurkey
  2. 2.Faculty of Engineering, Mechanical Engineering DepartmentNear East UniversityNicosiaTurkey

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