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A novel adaptative maximum power point tracking algorithm for small wind turbines

Author

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  • Kortabarria, Iñigo
  • Andreu, Jon
  • Martínez de Alegría, Iñigo
  • Jiménez, Jaime
  • Gárate, José Ignacio
  • Robles, Eider

Abstract

A novel maximum power point tracking algorithm for small wind turbines is proposed. The solution presented here is an adaptive intelligent algorithm that uses a new advanced perturb and observe method to search for the optimum relationship of the system for tracking the maximum power point even under variable wind conditions. The validity of the proposed algorithm is analysed and the design procedure is presented. Its main virtue resides in its capability to adapt to changes in the turbine and in the surrounding environment, even under variable wind conditions, improving the efficiency of the system. The experimental results confirm the validity of the proposed algorithm.

Suggested Citation

  • Kortabarria, Iñigo & Andreu, Jon & Martínez de Alegría, Iñigo & Jiménez, Jaime & Gárate, José Ignacio & Robles, Eider, 2014. "A novel adaptative maximum power point tracking algorithm for small wind turbines," Renewable Energy, Elsevier, vol. 63(C), pages 785-796.
    • Handle: RePEc:eee:renene:v:63:y:2014:i:c:p:785-796
    DOI: 10.1016/j.renene.2013.10.036
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    References listed on IDEAS

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    1. Ram, J.Prasanth & Rajasekar, N. & Miyatake, Masafumi, 2017. "Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1138-1159.
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    3. De Kooning, Jeroen D.M. & Vandoorn, Tine L. & Van de Vyver, Jan & Meersman, Bart & Vandevelde, Lieven, 2016. "Displacement of the maximum power point caused by losses in wind turbine systems," Renewable Energy, Elsevier, vol. 85(C), pages 273-280.
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    5. Dixon, Christopher & Reynolds, Steve & Rodley, David, 2016. "Micro/small wind turbine power control for electrolysis applications," Renewable Energy, Elsevier, vol. 87(P1), pages 182-192.
    6. 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.
    7. Tania García-Sánchez & Arbinda Kumar Mishra & Elías Hurtado-Pérez & Rubén Puché-Panadero & Ana Fernández-Guillamón, 2020. "A Controller for Optimum Electrical Power Extraction from a Small Grid-Interconnected Wind Turbine," Energies, MDPI, vol. 13(21), pages 1-16, November.
    8. 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.
    9. Karabacak, Murat, 2019. "A new perturb and observe based higher order sliding mode MPPT control of wind turbines eliminating the rotor inertial effect," Renewable Energy, Elsevier, vol. 133(C), pages 807-827.
    10. Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.
    11. Xuan Chau Le & Minh Quan Duong & Kim Hung Le, 2022. "Review of the Modern Maximum Power Tracking Algorithms for Permanent Magnet Synchronous Generator of Wind Power Conversion Systems," Energies, MDPI, vol. 16(1), pages 1-25, December.
    12. Maheshwari, Zeel & Kengne, Kamgang & Bhat, Omkar, 2023. "A comprehensive review on wind turbine emulators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).

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