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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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    1. Pope, K. & Dincer, I. & Naterer, G.F., 2010. "Energy and exergy efficiency comparison of horizontal and vertical axis wind turbines," Renewable Energy, Elsevier, vol. 35(9), pages 2102-2113.
    2. Nikolić, Vlastimir & Petković, Dalibor & Shamshirband, Shahaboddin & Ćojbašić, Žarko, 2015. "Adaptive neuro-fuzzy estimation of diffuser effects on wind turbine performance," Energy, Elsevier, vol. 89(C), pages 324-333.
    3. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    4. Eriksson, Sandra & Bernhoff, Hans & Leijon, Mats, 2008. "Evaluation of different turbine concepts for wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(5), pages 1419-1434, June.
    5. Yuji Ohya & Takashi Karasudani, 2010. "A Shrouded Wind Turbine Generating High Output Power with Wind-lens Technology," Energies, MDPI, vol. 3(4), pages 1-16, March.
    6. Shamshirband, Shahaboddin & Petković, Dalibor & Anuar, Nor Badrul & Gani, Abdullah, 2014. "Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 270-276.
    7. Aslam Bhutta, Muhammad Mahmood & Hayat, Nasir & Farooq, Ahmed Uzair & Ali, Zain & Jamil, Sh. Rehan & Hussain, Zahid, 2012. "Vertical axis wind turbine – A review of various configurations and design techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1926-1939.
    8. Grassmann, H. & Bet, F. & Cabras, G. & Ceschia, M. & Cobai, D. & DelPapa, C., 2003. "A partially static turbine—first experimental results," Renewable Energy, Elsevier, vol. 28(11), pages 1779-1785.
    9. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
    10. Chong, W.T. & Pan, K.C. & Poh, S.C. & Fazlizan, A. & Oon, C.S. & Badarudin, A. & Nik-Ghazali, N., 2013. "Performance investigation of a power augmented vertical axis wind turbine for urban high-rise application," Renewable Energy, Elsevier, vol. 51(C), pages 388-397.
    11. Cadenas, Erasmo & Rivera, Wilfrido, 2007. "Wind speed forecasting in the South Coast of Oaxaca, México," Renewable Energy, Elsevier, vol. 32(12), pages 2116-2128.
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    5. Khani, Mohammad Sadegh & Shahsavani, Younes & Mehraein, Mojtaba & Soleimani Rad, Mohammad Hossein & Nikbakhsh, Amir Abbas, 2024. "Evaluation of the performance of the Savonius hydrokinetic turbines in the straight and curved channels using advanced machine learning methods," Energy, Elsevier, vol. 290(C).
    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.
    9. Mohammadi, M. & Lakestani, M. & Mohamed, M.H., 2018. "Intelligent parameter optimization of Savonius rotor using Artificial Neural Network and Genetic Algorithm," Energy, Elsevier, vol. 143(C), pages 56-68.

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