IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i6p3793-3809d37220.html
   My bibliography  Save this article

Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms

Author

Listed:
  • Asier González-González

    (Tecnalia Research & Innovation, Industry and Transport Division, Parque Tecnológico de Álava, c/ Albert Einstein 28, Miñano 01510, Spain)

  • Ismael Etxeberria-Agiriano

    (Department of Computer Languages and Systems, University College of Engineering, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain)

  • Ekaitz Zulueta

    (Department of Systems Engineering & Automatic Control, University College of Engineering, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain)

  • Fernando Oterino-Echavarri

    (Department of Electronic Technology, University College of Engineering, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain)

  • Jose Manuel Lopez-Guede

    (Department of Systems Engineering & Automatic Control, University College of Engineering, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain)

Abstract

This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO)-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration.

Suggested Citation

  • Asier González-González & Ismael Etxeberria-Agiriano & Ekaitz Zulueta & Fernando Oterino-Echavarri & Jose Manuel Lopez-Guede, 2014. "Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms," Energies, MDPI, vol. 7(6), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:6:p:3793-3809:d:37220
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/6/3793/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/7/6/3793/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xin Cai & Jie Zhu & Pan Pan & Rongrong Gu, 2012. "Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method," Energies, MDPI, vol. 5(11), pages 1-14, November.
    2. Jae-Kun Lyu & Jae-Haeng Heo & Jong-Keun Park & Yong-Cheol Kang, 2013. "Probabilistic Approach to Optimizing Active and Reactive Power Flow in Wind Farms Considering Wake Effects," Energies, MDPI, vol. 6(11), pages 1-21, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Igor Ansoategui & Ekaitz Zulueta & Unai Fernandez-Gamiz & Jose Manuel Lopez-Guede, 2019. "Mechatronic Modeling and Frequency Analysis of the Drive Train of a Horizontal Wind Turbine," Energies, MDPI, vol. 12(4), pages 1-14, February.
    2. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    3. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.
    4. Pablo Zambrana & Javier Fernandez-Quijano & J. Jesus Fernandez-Lozano & Pedro M. Mayorga Rubio & Alfonso J. Garcia-Cerezo, 2021. "Improving the Performance of Controllers for Wind Turbines on Semi-Submersible Offshore Platforms: Fuzzy Supervisor Control," Energies, MDPI, vol. 14(19), pages 1-17, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liang Lu & Minyan Zhu & Haijun Wu & Jianzhong Wu, 2022. "A Review and Case Analysis on Biaxial Synchronous Loading Technology and Fast Moment-Matching Methods for Fatigue Tests of Wind Turbine Blades," Energies, MDPI, vol. 15(13), pages 1-34, July.
    2. Gracita Batista Rosas & Elizete Maria Lourenço & Djalma Mosqueira Falcão & Thelma Solange Piazza Fernandes, 2019. "An Expeditious Methodology to Assess the Effects of Intermittent Generation on Power Systems," Energies, MDPI, vol. 12(6), pages 1-18, March.
    3. Jie Zhu & Xin Cai & Pan Pan & Rongrong Gu, 2014. "Multi-Objective Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using the Non-Dominated Sorting Genetic Algorithm II and Finite Element Method," Energies, MDPI, vol. 7(2), pages 1-15, February.
    4. Khazar Hayat & Shafaqat Siddique & Tipu Sultan & Hafiz T. Ali & Fahed A. Aloufi & Riyadh F. Halawani, 2022. "Effect of Spar Design Optimization on the Mass and Cost of a Large-Scale Composite Wind Turbine Blade," Energies, MDPI, vol. 15(15), pages 1-17, August.
    5. Lu, Liang & Wu, Haijun & Wu, Jianzhong, 2021. "A case study for the optimization of moment-matching in wind turbine blade fatigue tests with a resonant type exciting approach," Renewable Energy, Elsevier, vol. 174(C), pages 769-785.
    6. Yue Chen & Zhizhong Guo & Hongbo Li & Yi Yang & Abebe Tilahun Tadie & Guizhong Wang & Yingwei Hou, 2020. "Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    7. Jie Zhu & Xin Cai & Rongrong Gu, 2016. "Aerodynamic and Structural Integrated Optimization Design of Horizontal-Axis Wind Turbine Blades," Energies, MDPI, vol. 9(2), pages 1-18, January.
    8. Shuli Wen & Hai Lan & Qiang Fu & David C. Yu & Ying-Yi Hong & Peng Cheng, 2017. "Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms," Energies, MDPI, vol. 10(5), pages 1-16, May.
    9. Walter M. Villa-Acevedo & Jesús M. López-Lezama & Jaime A. Valencia-Velásquez, 2018. "A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem," Energies, MDPI, vol. 11(9), pages 1-23, September.
    10. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:7:y:2014:i:6:p:3793-3809:d:37220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.