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Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control

Author

Listed:
  • Jesús Enrique Sierra-García

    (Electromechanical Engineering Department, University of Burgos, 09006 Burgos, Spain)

  • Matilde Santos

    (Institute of Knowledge Technology, Complutense University of Madrid, 28040 Madrid, Spain)

Abstract

Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error.

Suggested Citation

  • Jesús Enrique Sierra-García & Matilde Santos, 2021. "Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3235-:d:517481
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    References listed on IDEAS

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    1. 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.
    2. Silvio Simani & Paolo Castaldi, 2020. "Fault Diagnosis Techniques for a Wind Turbine System," Chapters, in: Fausto Pedro Garcia Marquez (ed.), Fault Detection, Diagnosis and Prognosis, IntechOpen.
    3. Mansoor Khan & Tianqi Liu & Farhan Ullah, 2019. "A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis," Energies, MDPI, vol. 12(12), pages 1-21, June.
    4. Moodi, Hoda & Bustan, Danyal, 2019. "Wind turbine control using T-S systems with nonlinear consequent parts," Energy, Elsevier, vol. 172(C), pages 922-931.
    5. Hao Duan & Ming Lu & Yongteng Sun & Jinyu Wang & Cheng Wang & Zuguo Chen, 2020. "Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE," Complexity, Hindawi, vol. 2020, pages 1-11, July.
    6. J. Enrique Sierra-García & Matilde Santos, 2020. "Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning," Complexity, Hindawi, vol. 2020, pages 1-15, September.
    7. Mikati, M. & Santos, M. & Armenta, C., 2013. "Electric grid dependence on the configuration of a small-scale wind and solar power hybrid system," Renewable Energy, Elsevier, vol. 57(C), pages 587-593.
    8. Zi Lin & Xiaolei Liu, 2020. "Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning," Energies, MDPI, vol. 13(9), pages 1-21, May.
    9. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.
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    Cited by:

    1. Gökalp, E. & Gülpınar, N. & Doan, X.V., 2023. "Dynamic surgery management under uncertainty," European Journal of Operational Research, Elsevier, vol. 309(2), pages 832-844.

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