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Adaptive neuro-fuzzy estimation of diffuser effects on wind turbine performance

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  • Nikolić, Vlastimir
  • Petković, Dalibor
  • Shamshirband, Shahaboddin
  • Ćojbašić, Žarko

Abstract

Wind power is generating interest amongst many countries to produce sustainable electrical power. It is well known that the main drawback of wind power is the inherent variable behavior of wind speed. Significant research has been carried out to improve the performance of the wind turbines and establish the power system stability. As power output is proportional to the cubic power of the incident airspeed, any small increase in the incident wind yields a large increase in the energy output. One of the more promising advanced concepts for overcoming the inherent variable behavior of wind speed is the DAWT (diffuser-augmented wind turbine). The diffuser or flanged diffuser generates separation regions behind it, where low-pressure regions appear to draw more wind through the rotors compared to a bare wind turbine. Thus, the output power of the DAWT is much larger than for a unshrouded turbine. To estimate rotor performance of the diffuser-augmented wind turbine, this paper constructed a process which simulates the power output, torque output and rotational speed of the rotor in regard to diffuser effect and wind input speed with ANFIS (adaptive neuro-fuzzy) method. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:324-333
    DOI: 10.1016/j.energy.2015.05.126
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    2. 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).
    3. Halabi, Laith M. & Mekhilef, Saad & Hossain, Monowar, 2018. "Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation," Applied Energy, Elsevier, vol. 213(C), pages 247-261.
    4. Yazar, Isil & Yavuz, Hasan Serhan & Yavuz, Arzu Altin, 2017. "Comparison of various regression models for predicting compressor and turbine performance parameters," Energy, Elsevier, vol. 140(P2), pages 1398-1406.
    5. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.

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