IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v102y2016icp630-636.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421630144X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.02.082?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Al-Sulaiman, Fahad A., 2017. "Exergoeconomic analysis of ejector-augmented shrouded wind turbines," Energy, Elsevier, vol. 128(C), pages 264-270.
    2. 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.
    3. Govind, Bala, 2017. "Increasing the operational capability of a horizontal axis wind turbine by its integration with a vertical axis wind turbine," Applied Energy, Elsevier, vol. 199(C), pages 479-494.
    4. Khani, Mohammad Sadegh & Shahsavani, Younes & Mehraein, Mojtaba & Kisi, Ozgur, 2023. "Performance evaluation of the savonius hydrokinetic turbine using soft computing techniques," Renewable Energy, Elsevier, vol. 215(C).
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wong, Kok Hoe & Chong, Wen Tong & Sukiman, Nazatul Liana & Poh, Sin Chew & Shiah, Yui-Chuin & Wang, Chin-Tsan, 2017. "Performance enhancements on vertical axis wind turbines using flow augmentation systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 904-921.
    2. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    3. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    4. Rahman, Mahmudur & Ong, Zhi Chao & Chong, Wen Tong & Julai, Sabariah & Khoo, Shin Yee, 2015. "Performance enhancement of wind turbine systems with vibration control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 43-54.
    5. Kumar, Rakesh & Raahemifar, Kaamran & Fung, Alan S., 2018. "A critical review of vertical axis wind turbines for urban applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 281-291.
    6. 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.
    7. Hamdan, A. & Mustapha, F. & Ahmad, K.A. & Mohd Rafie, A.S., 2014. "A review on the micro energy harvester in Structural Health Monitoring (SHM) of biocomposite material for Vertical Axis Wind Turbine (VAWT) system: A Malaysia perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 23-30.
    8. Guo, Lukai & Wang, Hao, 2022. "Non-intrusive movable energy harvesting devices: Materials, designs, and their prospective uses on transportation infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    9. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    10. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
    11. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    12. Ahmad Fazlizan & Wen Tong Chong & Sook Yee Yip & Wooi Ping Hew & Sin Chew Poh, 2015. "Design and Experimental Analysis of an Exhaust Air Energy Recovery Wind Turbine Generator," Energies, MDPI, vol. 8(7), pages 1-19, June.
    13. Hand, Brian & Kelly, Ger & Cashman, Andrew, 2021. "Aerodynamic design and performance parameters of a lift-type vertical axis wind turbine: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    14. Hesami, Ali & Nikseresht, Amir H., 2023. "Towards development and optimization of the Savonius wind turbine incorporated with a wind-lens," Energy, Elsevier, vol. 274(C).
    15. Pagnini, Luisa C. & Burlando, Massimiliano & Repetto, Maria Pia, 2015. "Experimental power curve of small-size wind turbines in turbulent urban environment," Applied Energy, Elsevier, vol. 154(C), pages 112-121.
    16. Poguluri, Sunny Kumar & Lee, Hyebin & Bae, Yoon Hyeok, 2021. "An investigation on the aerodynamic performance of a co-axial contra-rotating vertical-axis wind turbine," Energy, Elsevier, vol. 219(C).
    17. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    18. Guo, Fen & Song, Baowei & Mao, Zhaoyong & Tian, Wenlong, 2020. "Experimental and numerical validation of the influence on Savonius turbine caused by rear deflector," Energy, Elsevier, vol. 196(C).
    19. Liu, Jinfu & Ren, Guorui & Wan, Jie & Guo, Yufeng & Yu, Daren, 2016. "Variogram time-series analysis of wind speed," Renewable Energy, Elsevier, vol. 99(C), pages 483-491.
    20. Leloudas, Stavros N. & Lygidakis, Georgios N. & Eskantar, Alexandros I. & Nikolos, Ioannis K., 2020. "A robust methodology for the design optimization of diffuser augmented wind turbine shrouds," Renewable Energy, Elsevier, vol. 150(C), pages 722-742.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:102:y:2016:i:c:p:630-636. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.