Skip to main content
Springer Nature Link
Log in

Control Tuning by Genetic Algorithm of a Low Scale Model Wind Turbine

  • Conference paper
  • First Online:
17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

  • 673 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Sierra-García, J.E., Santos, M.: Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial 18(4), 327–335 (2021)

    Article  Google Scholar 

  2. Sierra-García, J.E., Santos, M.: Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning, 2020, Complexity (2020)

    Google Scholar 

  3. Andrade, G.A., Esteban, S.: Modelo a escala de aerogenerador para control. In: Aitor J. Garrido et al. (eds.) Innovation and Lecture Notes In Control Engineering For Clean Energy Generation, pp. 53–58. Universidad del País Vasco, Bilbao (2021)

    Google Scholar 

  4. López, R., Santos, M., Polo, O., Esteban, S.: Experimenting a fuzzy controller on a fast ferry. In: Proceedings of the IEEE Int. Conf. on Control Applications CCA, vol. 2, pp. 1082–1087. IEEE (2002)

    Google Scholar 

  5. Alzayed, M., Chaoui, H., Farajpour, Y.: Maximum power tracking for a wind energy conversion system using cascade-forward neural networks. IEEE Trans. Sustain. Energy 12(4), 2367–2377 (2021)

    Article  Google Scholar 

  6. Sierra-García, J.E., Santos, M.: Lookup table and neural network hybrid strategy for wind turbine pitch control. Sustainability 13(6), 3235 (2021)

    Article  Google Scholar 

  7. Chavero-Navarrete, E., Trejo-Perea, M., Jáuregui-Correa, J.C., Carrillo-Serrano, R.V., Ronquillo-Lomeli, G., Ríos-Moreno, J.G.: Hierarchical pitch control for small wind turbines based on fuzzy logic and anticipated wind speed measurement. Appl. Sci. 10(13), 4592 (2020)

    Google Scholar 

  8. ARDUINO Store, http://store.arduino.cc/products/arduino-nano-33-iot. Accessed 25 Feb 2022

  9. GITHUB, TFGGiordyAlexander, https://github.com/GGiordy/TFG_Giordy_Alexander.git. Accessed 25 Feb 2022

  10. Zotes, F.A., Penas, M.S.: Multi-criteria genetic optimisation of the manoeuvres of a two-stage launcher. Inf. Sci. 180(6), 896–910 (2010)

    Article  Google Scholar 

  11. Villoslada, D., Santos, M., Tomás-Rodríguez, M.: General methodology for the identification of reduced dynamic models of barge-type floating wind turbines. Energies 14(13), 3902 (2021)

    Article  Google Scholar 

  12. Torralba-Morales, L.M., Reynoso-Meza, G., Carrillo-Ahumada, J.: Tuning and comparison of design concepts applying Pareto optimality. a case study of cholette bioreactor. Revista Iberoamericana de Automática e Informática Industrial, 17(2), 190–201 (2020)

    Google Scholar 

Download references

Acknowledgments

This work has been partially supported by the Spanish Ministry of Science and Innovation under the project MCI/AEI/FEDER number RTI2018–094902-B-C21.

Author information

Authors and Affiliations

  1. Faculty of Physics, UCM, 28840, Madrid, Spain

    Giordy Alexander Andrade Aimara

  2. DACYA, Faculty of Physics, UCM, 28840, Madrid, Spain

    Segundo Esteban San Román

  3. Institute of Knowledge Technology, UCM, 28840, Madrid, Spain

    Matilde Santos

Authors
  1. Giordy Alexander Andrade Aimara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Segundo Esteban San Román
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matilde Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giordy Alexander Andrade Aimara .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

Publish with us

Policies and ethics