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Application of adaptive neuro-fuzzy methodology for performance investigation of a power-augmented vertical axis wind turbine

Author

Listed:
  • Chong, W.T.
  • Gwani, M.
  • Shamshirband, S.
  • Muzammil, W.K.
  • Tan, C.J.
  • Fazlizan, A.
  • Poh, S.C.
  • Petković, Dalibor
  • Wong, K.H.

Abstract

Wind power is generating a lot of interest in many countries as a way to produce sustainable and low-cost electrical power. Since the power in the wind is known to be proportional to the cubic power of the wind velocity approaching the wind turbine, this means that any slight increase in wind speed can lead to a substantial increment in the energy output. Power augmentation device is an interesting option in this respect. The aim of this study is to determine the accuracy of a soft computing technique on the rotational speed estimation of a Sistan rotor vertical axis wind turbine with PAGV (power-augmentation-guide-vane) based upon a series of measurements. An ANFIS (adaptive neuro-fuzzy inference system) was used to predict the wind turbine rotational speed. The ANFIS network was developed with three neurons in the input layer, and one neuron in the output layer. The inputs for the network were time (t), wind velocity (v) and presence of the PAGV (0 with PAGV and 1 without PAGV). The precision of ANFIS technique was assessed against the experimental results using RMSE (root-mean-square error) and coefficient of determination (R2).

Suggested Citation

  • Chong, W.T. & Gwani, M. & Shamshirband, S. & Muzammil, W.K. & Tan, C.J. & Fazlizan, A. & Poh, S.C. & Petković, Dalibor & Wong, K.H., 2016. "Application of adaptive neuro-fuzzy methodology for performance investigation of a power-augmented vertical axis wind turbine," Energy, Elsevier, vol. 102(C), pages 630-636.
    • Handle: RePEc:eee:energy:v:102:y:2016:i:c:p:630-636
    DOI: 10.1016/j.energy.2016.02.082
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    Cited by:

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    5. Tahani, Mojtaba & Rabbani, Ali & Kasaeian, Alibakhsh & Mehrpooya, Mehdi & Mirhosseini, Mojtaba, 2017. "Design and numerical investigation of Savonius wind turbine with discharge flow directing capability," Energy, Elsevier, vol. 130(C), pages 327-338.
    6. Alom, Nur & Saha, Ujjwal K., 2018. "Performance evaluation of vent-augmented elliptical-bladed savonius rotors by numerical simulation and wind tunnel experiments," Energy, Elsevier, vol. 152(C), pages 277-290.
    7. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
    8. Grönman, Aki & Backman, Jari & Hansen-Haug, Markus & Laaksonen, Mikko & Alkki, Markku & Aura, Pekka, 2018. "Experimental and numerical analysis of vaned wind turbine performance and flow phenomena," Energy, Elsevier, vol. 159(C), pages 827-841.

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