IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v99y2016icp996-1007.html
   My bibliography  Save this article

LPV-based active power control of wind turbines covering the complete wind speed range

Author

Listed:
  • Inthamoussou, Fernando A.
  • De Battista, Hernán
  • Mantz, Ricardo J.

Abstract

This paper focuses on the active power control of wind turbines. Modern grid codes increasingly demand active power control in order to guarantee utility grid stability under high wind energy penetration. Active power control provides capabilities to regulate wind power below rated, to maintain a power reserve, to indirectly regulate the grid frequency, etc. New controllers are necessary to tackle the extended operating modes and new objectives. This paper addresses the control problem using LPV techniques, since they are particularly suited to cope with the nonlinearities that arise along the extended operating region. The proposed controller was evaluated on a 5 MW wind turbine benchmark. For that purpose, very demanding and realistic testing scenarios were built using the FAST aeroelastic wind turbine simulator as well as standardized wind speed profiles. The proposed controller was compared with the gain scheduled PI traditionally used for wind turbine control and also with a gain scheduled ℋ∞ controller. Finally, a comparative load analysis is presented with the aim of showing that the softer and lower pitch activity of the proposed controller decreases the extreme load events.

Suggested Citation

  • Inthamoussou, Fernando A. & De Battista, Hernán & Mantz, Ricardo J., 2016. "LPV-based active power control of wind turbines covering the complete wind speed range," Renewable Energy, Elsevier, vol. 99(C), pages 996-1007.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:996-1007
    DOI: 10.1016/j.renene.2016.07.064
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116306796
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.07.064?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bianchi, F.D. & Sánchez-Peña, R.S. & Guadayol, M., 2012. "Gain scheduled control based on high fidelity local wind turbine models," Renewable Energy, Elsevier, vol. 37(1), pages 233-240.
    2. Bianchi, F.D. & Mantz, R.J. & Christiansen, C.F., 2004. "Power regulation in pitch-controlled variable-speed WECS above rated wind speed," Renewable Energy, Elsevier, vol. 29(11), pages 1911-1922.
    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. Yarmohammadi, Mohammad J. & Sadeghzadeh, Arash & Taghizadeh, Mostafa, 2020. "Gain-scheduled control of wind turbine exploiting inexact wind speed measurement for full operating range," Renewable Energy, Elsevier, vol. 149(C), pages 890-901.
    2. Levieux, Luis Ignacio & Ocampo-Martinez, Carlos & Inthamoussou, Fernando A. & De Battista, Hernán, 2021. "Predictive management approach for the coordination of wind and water-based power supplies," Energy, Elsevier, vol. 219(C).
    3. Barambones, Oscar & Cortajarena, Jose A. & Calvo, Isidro & Gonzalez de Durana, Jose M. & Alkorta, Patxi & Karami-Mollaee, A., 2019. "Variable speed wind turbine control scheme using a robust wind torque estimation," Renewable Energy, Elsevier, vol. 133(C), pages 354-366.
    4. Yao, Qi & Hu, Yang & Deng, Hui & Luo, Zhiling & Liu, Jizhen, 2020. "Two-degree-of-freedom active power control of megawatt wind turbine considering fatigue load optimization," Renewable Energy, Elsevier, vol. 162(C), pages 2096-2112.
    5. Dali, Ali & Abdelmalek, Samir & Bakdi, Azzeddine & Bettayeb, Maamar, 2021. "A new robust control scheme: Application for MPP tracking of a PMSG-based variable-speed wind turbine," Renewable Energy, Elsevier, vol. 172(C), pages 1021-1034.

    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. Sales-Setién, Ester & Peñarrocha-Alós, Ignacio, 2020. "Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level," Renewable Energy, Elsevier, vol. 146(C), pages 1746-1765.
    2. Moayed Almobaied & Hassan S. Al-Nahhal & Orlando Arrieta & Ramon Vilanova, 2023. "Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System," Mathematics, MDPI, vol. 11(19), pages 1-21, September.
    3. Howlader, Abdul Motin & Izumi, Yuya & Uehara, Akie & Urasaki, Naomitsu & Senjyu, Tomonobu & Yona, Atsushi & Saber, Ahmed Yousuf, 2012. "A minimal order observer based frequency control strategy for an integrated wind-battery-diesel power system," Energy, Elsevier, vol. 46(1), pages 168-178.
    4. Moradi, Hamed & Vossoughi, Gholamreza, 2015. "Robust control of the variable speed wind turbines in the presence of uncertainties: A comparison between H∞ and PID controllers," Energy, Elsevier, vol. 90(P2), pages 1508-1521.
    5. Sanchez, Ismael, 2006. "Short-term prediction of wind energy production," International Journal of Forecasting, Elsevier, vol. 22(1), pages 43-56.
    6. Taveiros, F.E.V. & Barros, L.S. & Costa, F.B., 2015. "Back-to-back converter state-feedback control of DFIG (doubly-fed induction generator)-based wind turbines," Energy, Elsevier, vol. 89(C), pages 896-906.
    7. David Rodriguez-Rosa & Ismael Payo-Gutierrez & Fernando J. Castillo-Garcia & Antonio Gonzalez-Rodriguez & Sergio Perez-Juarez, 2017. "Improving Energy Efficiency of an Autonomous Bicycle with Adaptive Controller Design," Sustainability, MDPI, vol. 9(5), pages 1-16, May.
    8. La Cava, William & Danai, Kourosh & Spector, Lee & Fleming, Paul & Wright, Alan & Lackner, Matthew, 2016. "Automatic identification of wind turbine models using evolutionary multiobjective optimization," Renewable Energy, Elsevier, vol. 87(P2), pages 892-902.

    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:eee:renene:v:99:y:2016:i:c:p:996-1007. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.