Sintonización basada en datos de un control difuso mediante técnicas de optimización evolutiva en turbinas eólicas flotantes

  1. Hernández Hernández, César Ernesto 1
  2. Sierra García, Jesús Enrique 2
  3. Santos, Matilde 3
  1. 1 UNED. Universidad Nacional de Educación a Distancia
  2. 2 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  3. 3 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

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Buch:
XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja)
  1. Carlos Balaguer Bernaldo de Quirós (coord.)
  2. José Manuel Andújar Márquez (coord.)
  3. Ramon Costa Castelló (coord.)
  4. Carlos Ocampo Martínez (coord.)
  5. Jesús Fernández Lozano (coord.)
  6. Matilde Santos Peñas (coord.)
  7. José Enrique Simó Ten (coord.)
  8. Montserrat Gil Martínez (coord.)
  9. Jose Luis Calvo Rolle (coord.)
  10. Raúl Marín Prades (coord.)
  11. Eduardo Rocón de Lima (coord.)
  12. Elisabet Estévez Estévez (coord.)
  13. Pedro Jesús Cabrera Santana (coord.)
  14. David Muñoz de la Peña Sequedo (coord.)
  15. José Luis Guzmán Sánchez (coord.)
  16. José Luis Pitarch Pérez (coord.)
  17. Oscar Reinoso García (coord.)
  18. Oscar Déniz Suárez (coord.)
  19. Emilio Jiménez Macías (coord.)
  20. Vanesa Loureiro Vázquez (coord.)

Verlag: Servizo de Publicacións ; Universidade da Coruña

ISBN: 978-84-9749-841-8

Datum der Publikation: 2022

Seiten: 208-215

Kongress: Jornadas de Automática (43. 2022. Logroño)

Art: Konferenz-Beitrag

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Zusammenfassung

This paper presents a method for tuning a PI pitch control and a fuzzy pitch control for floating offshore wind turbines in order to maximise energy production and reduce vibrations. Due to the complexity of the system, several control techniques have been applied to find efficient solutions to improve the productivity of these renewable energy devices. The PI control is tuned by random generation of the gains KP and KI through multiple simulations, and a data-driven tuning of a fuzzy control using optimisation algorithms: particle swarm optimization and patter search optimization, is also presented. The proposed controllers have been applied to NREL’s 5 MW barge-type floating wind turbine. Both controllers have been compared with the controller integrated in FAST, a gain-scheduled PI (GS-PI) controller, giving better results in terms of rated power error and lower tower displacement and platform oscillations.

Fuente de los datos: Dialnet