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Adaptive neuro-fuzzy approach for ducted tidal turbine performance estimation

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  • Anicic, Obrad
  • Jovic, Srdjan

Abstract

The potential of marine power to produce electricity has been exploited recently. One of the ways of producing electricity is by using tidal current turbines. In this study, the hydrodynamic performance of a novel type of ducted tidal turbine was investigated. Tidal energy has become a large contender of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized tidal turbines. Hence, quality of produced energy becomes an important problem in tidal energy conversion plants. Several control techniques have been applied to improve the quality of power generated from tidal turbines. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate power coefficient value of the ducted tidal turbines. The back propagation learning algorithm is used for training this network. This intelligent controller is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Suggested Citation

  • Anicic, Obrad & Jovic, Srdjan, 2016. "Adaptive neuro-fuzzy approach for ducted tidal turbine performance estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1111-1116.
    • Handle: RePEc:eee:rensus:v:59:y:2016:i:c:p:1111-1116
    DOI: 10.1016/j.rser.2016.01.031
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    References listed on IDEAS

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    2. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.

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