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Comparative study of clustering methods for wake effect analysis in wind farm

Author

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  • Al-Shammari, Eiman Tamah
  • Shamshirband, Shahaboddin
  • Petković, Dalibor
  • Zalnezhad, Erfan
  • Yee, Por Lip
  • Taher, Ros Suraya
  • Ćojbašić, Žarko

Abstract

Wind energy poses challenges such as the reduction of the wind speed due to wake effect by other turbines. To increase wind farm efficiency, analyzing the parameters which have influence on the wake effect is very important. In this study clustering methods were applied on the wake effects in wind warm to separate district levels of the wake effects. To capture the patterns of the wake effects the PCA (principal component analysis) was applied. Afterwards, cluster analysis was used to analyze the clusters. FCM (Fuzzy c-means), K-mean, and K-medoids were used as the clustering algorithms. The main goal was to segment the wake effect levels in the wind farms. Ten different wake effect clusters were observed according to results. In other words the wake effect has 10 levels of influence on the wind farm energy production. Results show that the K-medoids method was more accurate than FCM and K-mean approach. K-medoid RMSE (root means square error) was 0.240 while the FCM and K-mean RMSEs were 0.320 and 1.509 respectively. The results can be used for wake effect levels segmentation in wind farms.

Suggested Citation

  • Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
  • Handle: RePEc:eee:energy:v:95:y:2016:i:c:p:573-579
    DOI: 10.1016/j.energy.2015.11.064
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    1. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    2. Mustakerov, Ivan & Borissova, Daniela, 2010. "Wind turbines type and number choice using combinatorial optimization," Renewable Energy, Elsevier, vol. 35(9), pages 1887-1894.
    3. Changshui, Zhang & Guangdong, Hou & Jun, Wang, 2011. "A fast algorithm based on the submodular property for optimization of wind turbine positioning," Renewable Energy, Elsevier, vol. 36(11), pages 2951-2958.
    4. Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2012. "Wake flow model of wind turbine using particle simulation," Renewable Energy, Elsevier, vol. 41(C), pages 185-190.
    5. Lignarolo, L.E.M. & Ragni, D. & Krishnaswami, C. & Chen, Q. & Simão Ferreira, C.J. & van Bussel, G.J.W., 2014. "Experimental analysis of the wake of a horizontal-axis wind-turbine model," Renewable Energy, Elsevier, vol. 70(C), pages 31-46.
    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. Yin, Peng-Yeng & Wang, Tai-Yuan, 2012. "A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms," Renewable Energy, Elsevier, vol. 48(C), pages 489-498.
    8. Saavedra-Moreno, B. & Salcedo-Sanz, S. & Paniagua-Tineo, A. & Prieto, L. & Portilla-Figueras, A., 2011. "Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms," Renewable Energy, Elsevier, vol. 36(11), pages 2838-2844.
    9. Nagai, Baku M. & Ameku, Kazumasa & Roy, Jitendro Nath, 2009. "Performance of a 3Â kW wind turbine generator with variable pitch control system," Applied Energy, Elsevier, vol. 86(9), pages 1774-1782, September.
    10. Subramanian, B. & Chokani, N. & Abhari, R.S., 2016. "Aerodynamics of wind turbine wakes in flat and complex terrains," Renewable Energy, Elsevier, vol. 85(C), pages 454-463.
    11. Petković, Dalibor & Ćojbašić, Žarko & Nikolić, Vlastimir & Shamshirband, Shahaboddin & Mat Kiah, Miss Laiha & Anuar, Nor Badrul & Abdul Wahab, Ainuddin Wahid, 2014. "Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission," Energy, Elsevier, vol. 64(C), pages 868-874.
    12. Lam, Wei-Haur & Chen, Long & Hashim, Roslan, 2015. "Analytical wake model of tidal current turbine," Energy, Elsevier, vol. 79(C), pages 512-521.
    Full references (including those not matched with items on IDEAS)

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