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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/6157

    Título
    Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
    Autor
    Sierra Garcia, Jesús EnriqueUBU authority Orcid
    Santos, Matilde
    Publicado en
    Neural Computing and Applications. 2022, V. 34, n. 13, p. 10503-10517
    Editorial
    Springer
    Fecha de publicación
    2022-07
    ISSN
    0941-0643
    DOI
    10.1007/s00521-021-06323-w
    Abstract
    This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.
    Palabras clave
    Hybrid system
    Deep learning
    Fuzzy control
    Neural networks
    Pitch control
    Wind turbines
    Materia
    Ingeniería mecánica
    Mechanical engineering
    URI
    http://hdl.handle.net/10259/6157
    Versión del editor
    https://doi.org/10.1007/s00521-021-06323-w
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