IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i12p8279-8293d43418.html
   My bibliography  Save this article

Fatigue Load Estimation through a Simple Stochastic Model

Author

Listed:
  • Pedro G. Lind

    (ForWind—Center for Wind Energy Research, Institute of Physics, Carl von Ossietzky University of Oldenburg, 26111 Oldenburg, Germany)

  • Iván Herráez

    (ForWind—Center for Wind Energy Research, Institute of Physics, Carl von Ossietzky University of Oldenburg, 26111 Oldenburg, Germany)

  • Matthias Wächter

    (ForWind—Center for Wind Energy Research, Institute of Physics, Carl von Ossietzky University of Oldenburg, 26111 Oldenburg, Germany)

  • Joachim Peinke

    (ForWind—Center for Wind Energy Research, Institute of Physics, Carl von Ossietzky University of Oldenburg, 26111 Oldenburg, Germany)

Abstract

We propose a procedure to estimate the fatigue loads on wind turbines, based on a recent framework used for reconstructing data series of stochastic properties measured at wind turbines. Through a standard fatigue analysis, we show that it is possible to accurately estimate fatigue loads in any wind turbine within one wind park, using only the load measurements at one single turbine and the set of wind speed measurements. Our framework consists of deriving a stochastic differential equation that describes the evolution of the torque at one wind turbine driven by the wind speed. The stochastic equation is derived directly from the measurements and is afterwards used for predicting the fatigue loads for neighboring turbines. Such a framework could be used to mitigate the financial efforts usually necessary for placing measurement devices in all wind turbines within one wind farm. Finally, we also discuss the limitations and possible improvements of the proposed procedure.

Suggested Citation

  • Pedro G. Lind & Iván Herráez & Matthias Wächter & Joachim Peinke, 2014. "Fatigue Load Estimation through a Simple Stochastic Model," Energies, MDPI, vol. 7(12), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:12:p:8279-8293:d:43418
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/12/8279/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/7/12/8279/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. So-Kumneth Sim & Philipp Maass & Pedro G. Lind, 2018. "Wind Speed Modeling by Nested ARIMA Processes," Energies, MDPI, vol. 12(1), pages 1-18, December.
    2. Akintayo Temiloluwa Abolude & Wen Zhou, 2018. "Assessment and Performance Evaluation of a Wind Turbine Power Output," Energies, MDPI, vol. 11(8), pages 1-15, August.
    3. Juhun Song & Hee-Chang Lim, 2019. "Study of Floating Wind Turbine with Modified Tension Leg Platform Placed in Regular Waves," Energies, MDPI, vol. 12(4), pages 1-18, February.
    4. Li, Liang & Liu, Yuanchuan & Yuan, Zhiming & Gao, Yan, 2018. "Wind field effect on the power generation and aerodynamic performance of offshore floating wind turbines," Energy, Elsevier, vol. 157(C), pages 379-390.
    5. Pedro G. Lind & Luis Vera-Tudela & Matthias Wächter & Martin Kühn & Joachim Peinke, 2017. "Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach," Energies, MDPI, vol. 10(12), pages 1-14, November.
    6. Ana Fernández-Guillamón & Guillermo Martínez-Lucas & Ángel Molina-García & Jose Ignacio Sarasua, 2020. "An Adaptive Control Scheme for Variable Speed Wind Turbines Providing Frequency Regulation in Isolated Power Systems with Thermal Generation," Energies, MDPI, vol. 13(13), pages 1-19, July.
    7. Nejra Beganovic & Jackson G. Njiri & Dirk Söffker, 2018. "Reduction of Structural Loads in Wind Turbines Based on an Adapted Control Strategy Concerning Online Fatigue Damage Evaluation Models," Energies, MDPI, vol. 11(12), pages 1-15, December.
    8. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.
    9. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.

    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:12:p:8279-8293:d:43418. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.