IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i17p3401-d263775.html
   My bibliography  Save this article

Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

Author

Listed:
  • Nicoletta Gioia

    (Department of Mechanical Engineering, Faculty of Applied Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium)

  • Cédric Peeters

    (Department of Mechanical Engineering, Faculty of Applied Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium)

  • Patrick Guillaume

    (Department of Mechanical Engineering, Faculty of Applied Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium)

  • Jan Helsen

    (Department of Mechanical Engineering, Faculty of Applied Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium)

Abstract

Noise, vibration and harshness (NVH) problems are critical issues to be tackled for wind turbine drivetrains. Tracking the behavior of modal parameters of the machines’ fundamental modes during operation it is of high interest to validate complex simulation models. A powerful approach for this purpose is represented by operational modal analysis (OMA). This paper describes the investigation of an automated technique for continuously tracking the modes of a rotating mechanical system running in normal operating conditions. The modal estimation procedure is based on an automatic version of the pLSCF (poly-reference Least-Square Complex Frequency-Domain) algorithm. The latter is coupled with a method that automatically tracks the modal parameters along different data sets. The use of OMA on a rotating component of the wind turbine creates the need to deal with harmonics in order to satisfy one of the assumptions of OMA. For this purpose, the use of a cepstrum editing procedure is analyzed and implemented. Modal estimates obtained from an automated analysis on stand still data and normal operating conditions data are compared, to test the added value of the cepstrum editing procedure and the robustness of the method when used on real data. To illustrate and validate the implemented methodology, data acquired during a long-term monitoring campaign of a wind turbine drivetrain are used.

Suggested Citation

  • Nicoletta Gioia & Cédric Peeters & Patrick Guillaume & Jan Helsen, 2019. "Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions," Energies, MDPI, vol. 12(17), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3401-:d:263775
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/17/3401/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/17/3401/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mahmoud El-Kafafy & Christof Devriendt & Patrick Guillaume & Jan Helsen, 2017. "Automatic Tracking of the Modal Parameters of an Offshore Wind Turbine Drivetrain System," Energies, MDPI, vol. 10(4), pages 1-15, April.
    Full references (including those not matched with items on IDEAS)

    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. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    2. Ying Wang & Wensheng Lu & Kaoshan Dai & Miaomiao Yuan & Shen-En Chen, 2018. "Dynamic Study of a Rooftop Vertical Axis Wind Turbine Tower Based on an Automated Vibration Data Processing Algorithm," Energies, MDPI, vol. 11(11), pages 1-21, November.

    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:12:y:2019:i:17:p:3401-:d:263775. 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.