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Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models

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  • Shamshirband, Shahaboddin
  • Petković, Dalibor
  • Anuar, Nor Badrul
  • Gani, Abdullah

Abstract

When the turbine extracts power from the wind, a wake evolves downstream of the turbine. The turbines operating in the wake are not only subjected to a decreased wind speed but also increased dynamic loading arising from the increased turbulence induced by the upstream turbines. This increased turbulence must be accounted, when selecting a turbine. This increase in turbulence intensity can imply a significant reduction in the fatigue lifetime of wind turbines placed in wakes. For this reason, a large number of studies have been established concerning the calculation of wake added turbulence. Even though a number of mathematical functions have been proposed for modeling the wake added turbulence, there are still disadvantages of the models like very demanding in terms of calculation time. Artificial neural networks (ANN) can be used as alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a specific type of the ANN family, was used to predict the wake added turbulence. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent algorithm 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

  • 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.
    • Handle: RePEc:eee:rensus:v:36:y:2014:i:c:p:270-276
    DOI: 10.1016/j.rser.2014.04.064
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    1. Shamshirband, Shahaboddin & Petković, Dalibor & Amini, Amineh & Anuar, Nor Badrul & Nikolić, Vlastimir & Ćojbašić, Žarko & Mat Kiah, Miss Laiha & Gani, Abdullah, 2014. "Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission," Energy, Elsevier, vol. 67(C), pages 623-630.
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    5. Shamshirband, Shahaboddin & Keivani, Afram & Mohammadi, Kasra & Lee, Malrey & Hamid, Siti Hafizah Abd & Petkovic, Dalibor, 2016. "Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 429-435.
    6. Wenbo Li & Dongyan Wang & Dan Yu & Yuefen Li & Shuhan Liu, 2016. "Forecasting Helianthus annuus Seed Quality Based on Soil Chemical Properties Using Radial Basis Function Neural Networks," Sustainability, MDPI, vol. 8(10), pages 1-9, October.
    7. Aghbashlo, Mortaza & Hosseinpour, Soleiman & Tabatabaei, Meisam & Younesi, Habibollah & Najafpour, Ghasem, 2016. "On the exergetic optimization of continuous photobiological hydrogen production using hybrid ANFIS–NSGA-II (adaptive neuro-fuzzy inference system–non-dominated sorting genetic algorithm-II)," Energy, Elsevier, vol. 96(C), pages 507-520.
    8. 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.
    9. Hashim, Roslan & Roy, Chandrabhushan & Motamedi, Shervin & Shamshirband, Shahaboddin & Petković, Dalibor, 2016. "Selection of climatic parameters affecting wave height prediction using an enhanced Takagi-Sugeno-based fuzzy methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 246-257.

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