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Synergies of Wind Turbine control techniques

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  • Bertašienė, Agnė
  • Azzopardi, Brian

Abstract

During the next decades, the market for Small-Scale Wind Turbines (SSWT) is expected to grow, due to a shift in micro-generation and current trends in distributed energy resources. Meanwhile in the last two decades, there were significant developments in control techniques for Large-Scale Wind Turbines (LSWT). Nonetheless, there exist synergies in Wind Turbine (WT) technologies from small to large scale. The reduction of WTs׳ operation and maintenance costs directly correlate to technical and economic matrices, which is crucial to the success of the wind energy industry. The aim of this paper is to compare WT control techniques from small to large scales levels, identifying common challenges and developments to achieve intelligent control algorithms at the small-to-medium scale levels. Therefore, the potential impact of increasing the competitiveness of wind energy in urban and suburban areas is explored and discussed through affordable and feasible levelised wind electricity costs.

Suggested Citation

  • Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
    • Handle: RePEc:eee:rensus:v:45:y:2015:i:c:p:336-342
    DOI: 10.1016/j.rser.2015.01.063
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    1. Meyer, David G. & Srinivasan, Sriram & Semrau, Greg, 2013. "Dynamic wind estimation based control for small wind turbines," Renewable Energy, Elsevier, vol. 50(C), pages 259-267.
    2. Saeidi, Davood & Sedaghat, Ahmad & Alamdari, Pourya & Alemrajabi, Ali Akbar, 2013. "Aerodynamic design and economical evaluation of site specific small vertical axis wind turbines," Applied Energy, Elsevier, vol. 101(C), pages 765-775.
    3. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    4. Hirahara, Hiroyuki & Hossain, M. Zakir & Kawahashi, Masaaki & Nonomura, Yoshitami, 2005. "Testing basic performance of a very small wind turbine designed for multi-purposes," Renewable Energy, Elsevier, vol. 30(8), pages 1279-1297.
    5. Mohandes, M. & Rehman, S. & Rahman, S.M., 2011. "Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)," Applied Energy, Elsevier, vol. 88(11), pages 4024-4032.
    6. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    7. Bououden, S. & Chadli, M. & Filali, S. & El Hajjaji, A., 2012. "Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach," Renewable Energy, Elsevier, vol. 37(1), pages 434-439.
    8. Lubitz, William David, 2014. "Impact of ambient turbulence on performance of a small wind turbine," Renewable Energy, Elsevier, vol. 61(C), pages 69-73.
    9. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    10. Arroyo, A. & Manana, M. & Gomez, C. & Fernandez, I. & Delgado, F. & Zobaa, Ahmed F., 2013. "A methodology for the low-cost optimisation of small wind turbine performance," Applied Energy, Elsevier, vol. 104(C), pages 1-9.
    11. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    12. Audierne, Etienne & Elizondo, Jorge & Bergami, Leonardo & Ibarra, Humberto & Probst, Oliver, 2010. "Analysis of the furling behavior of small wind turbines," Applied Energy, Elsevier, vol. 87(7), pages 2278-2292, July.
    13. Kusiak, Andrew & Zheng, Haiyang, 2010. "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, Elsevier, vol. 35(3), pages 1324-1332.
    14. Şerban, I. & Marinescu, C., 2012. "A sensorless control method for variable-speed small wind turbines," Renewable Energy, Elsevier, vol. 43(C), pages 256-266.
    15. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
    16. Islam, M.R. & Mekhilef, S. & Saidur, R., 2013. "Progress and recent trends of wind energy technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 456-468.
    17. Fischer, Gunter Reinald & Kipouros, Timoleon & Savill, Anthony Mark, 2014. "Multi-objective optimisation of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables," Renewable Energy, Elsevier, vol. 62(C), pages 506-515.
    18. Maki, Kevin & Sbragio, Ricardo & Vlahopoulos, Nickolas, 2012. "System design of a wind turbine using a multi-level optimization approach," Renewable Energy, Elsevier, vol. 43(C), pages 101-110.
    19. Douak, Fouzi & Melgani, Farid & Benoudjit, Nabil, 2013. "Kernel ridge regression with active learning for wind speed prediction," Applied Energy, Elsevier, vol. 103(C), pages 328-340.
    20. Monfared, Mohammad & Golestan, Saeed, 2012. "Control strategies for single-phase grid integration of small-scale renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4982-4993.
    21. 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.
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