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Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach

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  • Bououden, S.
  • Chadli, M.
  • Filali, S.
  • El Hajjaji, A.

Abstract

This paper deals with fuzzy model based multivariable predictive control (FMMPC) for wind turbine generator. Using Linear Matrix Inequalities (LMI) technique combined with predictive approach, a FMMPC law is designed easily by solving a convex optimization problem subject to LMI conditions. Then, such controller is developed to maintain a satisfactory quality of power in high wind speed operating region by reducing mechanical loads. The performances of the wind turbine in terms of wind turbine rotational speed and its stability, relative to wind speed changes, are discussed. The effectiveness and the merit of the proposed approach are shown by simulation results.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:37:y:2012:i:1:p:434-439
    DOI: 10.1016/j.renene.2011.06.025
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    References listed on IDEAS

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    1. Kusiak, Andrew & Li, Wenyan & Song, Zhe, 2010. "Dynamic control of wind turbines," Renewable Energy, Elsevier, vol. 35(2), pages 456-463.
    2. Kaldellis, J.K., 2008. "The wind potential impact on the maximum wind energy penetration in autonomous electrical grids," Renewable Energy, Elsevier, vol. 33(7), pages 1665-1677.
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    Cited by:

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    7. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    8. Jabbari Asl, Hamed & Yoon, Jungwon, 2016. "Power capture optimization of variable-speed wind turbines using an output feedback controller," Renewable Energy, Elsevier, vol. 86(C), pages 517-525.
    9. Petković, Dalibor & Ćojbašič, Žarko & Nikolić, Vlastimir, 2013. "Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 191-195.
    10. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
    11. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
    12. Yuan, Yuan & Chen, Xu & Tang, J., 2020. "Multivariable robust blade pitch control design to reject periodic loads on wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 329-341.
    13. Zineb Lahlou & Khaddouj Ben Meziane & Ismail Boumhidi, 2019. "Sliding mode controller based on type-2 fuzzy logic PID for a variable speed wind turbine," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 543-551, August.
    14. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    15. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
    16. Yancai Xiao & Tieling Zhang & Zeyu Ding & Chunya Li, 2016. "The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters," Energies, MDPI, vol. 9(5), pages 1-17, May.
    17. Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
    18. Moradi, Hamed & Vossoughi, Gholamreza, 2015. "Robust control of the variable speed wind turbines in the presence of uncertainties: A comparison between H∞ and PID controllers," Energy, Elsevier, vol. 90(P2), pages 1508-1521.
    19. Hongmin Meng & Tingting Yang & Ji-zhen Liu & Zhongwei Lin, 2017. "A Flexible Maximum Power Point Tracking Control Strategy Considering Both Conversion Efficiency and Power Fluctuation for Large-inertia Wind Turbines," Energies, MDPI, vol. 10(7), pages 1-19, July.
    20. Yin, Xiuxing & Pan, Li & Lei, Meizhen, 2022. "Maximizing tidal energy conversion by adopting hydraulic transformation and LMI based robust control," Renewable Energy, Elsevier, vol. 195(C), pages 331-343.
    21. Barooni, M. & Ale Ali, N. & Ashuri, T., 2018. "An open-source comprehensive numerical model for dynamic response and loads analysis of floating offshore wind turbines," Energy, Elsevier, vol. 154(C), pages 442-454.
    22. Narayana, Mahinsasa & Sunderland, Keith M. & Putrus, Ghanim & Conlon, Michael F., 2017. "Adaptive linear prediction for optimal control of wind turbines," Renewable Energy, Elsevier, vol. 113(C), pages 895-906.

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