RT Book, Section
T1 Control tuning by genetic algorithm of a low scale model wind turbine
A1 Andrade Aimara, Giordy Alexander
A1 Esteban San Román, Segundo
A1 Santos Peñas, Matilde
A2 García Bringas, P.
AB The continuous rise of wind energy makes it necessary to design controllers that make turbines more and more efficient. However, control designs are usually developed in simulation, which does not consider the many factors that affect control in a real turbine. An intermediate step, before testing them on turbines, is to check them on prototypes. In this paper, a first design of a laboratory scale model of a wind turbine (WT) is proposed, with the aim of testing different control algorithms. The model is built with commercial hardware and “ad hoc” circuits. Two control loops have been implemented; an external loop that commands the electric charge, and an internal loop that controls the pitch of the blades. The controllers have been tuned first experimentally, obtaining the best possible behavior by trial and error. Using genetic algorithms, the most optimal values for the controller are obtained, improving the response of the system. The operating and functional modes of a real WT have been also replicated on the model using a microcontroller programmed with the Arduino IDE input-output. Results obtained using optimized conventional controllers prove the correct performance of the prototype.
PB Springer
SN 978-3-031-18049-1
SN 2367-3370
YR 2023
FD 2023
LK https://hdl.handle.net/20.500.14352/116033
UL https://hdl.handle.net/20.500.14352/116033
LA eng
NO Andrade Aimara, G.A., Esteban San Román, S., Santos, M. (2023). Control Tuning by Genetic Algorithm of a Low Scale Model Wind Turbine. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_50
NO Ministerio de Ciencia e Innovación (España)
DS Docta Complutense
RD 4 abr 2025