2026-04-27T20:06:50Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/61572022-12-02T01:05:41Zcom_10259_6155com_10259_4266com_10259.4_106com_10259_2604col_10259_6156
Sierra Garcia, Jesús Enrique
737
500
0000-0001-6088-9954
Santos, Matilde
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2021-11-16T09:10:35Z
2021-11-16T09:10:35Z
2022-07
0941-0643
http://hdl.handle.net/10259/6157
10.1007/s00521-021-06323-w
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.
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by the Spanish Ministry of Science, Innovation and Universities under MCI/AEI/FEDER Project Number RTI2018-094902-B-C21.
Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE
eng
Springer
Neural Computing and Applications. 2022, V. 34, n. 13, p. 10503-10517
https://doi.org/10.1007/s00521-021-06323-w
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094902-B-C21/ES/ANALISIS Y CONTROL DE UN DISPOSITIVO FLOTANTE HIBRIDO DE ENERGIA EOLICA Y MARINA
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Ingeniería mecánica
Mechanical engineering
Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
THUMBNAIL
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oai:riubu.ubu.es:10259/6157
2022-12-02 02:05:41.126
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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