IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i8p2108-d349553.html
   My bibliography  Save this article

Fatigue Reliability Analysis of Wind Turbine Drivetrain Considering Strength Degradation and Load Sharing Using Survival Signature and FTA

Author

Listed:
  • Yao Li

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Caichao Zhu

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Xu Chen

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Jianjun Tan

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

Abstract

The wind turbine drivetrain suffers significant impact loads that severely affect the reliability and safety of wind turbines. Bearings and gears within the drivetrain are critical components with high repair costs and lengthy downtime. To realistically assess the system reliability, we propose to establish an electromechanical coupling dynamic model of the wind turbine considering the control strategy and environmental parameters and evaluate the system’s reliability of wind turbine drivetrain based on loads of gears and bearings. This paper focuses on the dynamic reliability analysis of the wind turbine under the control strategy and environmental conditions. SIMPACK (v9.7, Dassault Systèmes, Gilching, Germany) is used to develop the aero-hydro-servo-elastic coupling dynamic model with the full drivetrain that considers the flexibility of the tower and blade, the stochastic loads of wind and waves, gear meshing features, as well as the control strategy. The system reliability level of wind turbine drivetrain at different wind conditions is assessed using survival signature and fault tree analysis (FTA), and the influences of strength degradation of the transmission components on the system reliability are explored. Following this, the bending fatigue reliability and contact fatigue reliability concerning different wind conditions are compared in this paper. A case study is performed to demonstrate the effectiveness and feasibility of the proposed methodology.

Suggested Citation

  • Yao Li & Caichao Zhu & Xu Chen & Jianjun Tan, 2020. "Fatigue Reliability Analysis of Wind Turbine Drivetrain Considering Strength Degradation and Load Sharing Using Survival Signature and FTA," Energies, MDPI, vol. 13(8), pages 1-21, April.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2108-:d:349553
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/8/2108/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/8/2108/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Y. & Castro, A.M. & Martin, J.E. & Sinokrot, T. & Prescott, W. & Carrica, P.M., 2017. "Coupled computational fluid dynamics/multibody dynamics method for wind turbine aero-servo-elastic simulation including drivetrain dynamics," Renewable Energy, Elsevier, vol. 101(C), pages 1037-1051.
    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. Liao, Ding & Zhu, Shun-Peng & Correia, José A.F.O. & De Jesus, Abílio M.P. & Veljkovic, Milan & Berto, Filippo, 2022. "Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects," Renewable Energy, Elsevier, vol. 200(C), pages 724-742.
    2. Kristjanpoller, Fredy & Cárdenas-Pantoja, Nicolás & Viveros, Pablo & Pascual, Rodrigo, 2023. "Wind farm life cycle cost modelling based on oversizing capacity under load sharing configuration," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    3. Aiman Abbas Mahar & Nayyar Hussain Mirjat & Bhawani S. Chowdhry & Laveet Kumar & Quynh T. Tran & Gaetano Zizzo, 2023. "Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique," Energies, MDPI, vol. 16(5), pages 1-16, March.

    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. W. Dheelibun Remigius & Anand Natarajan, 2022. "A review of wind turbine drivetrain loads and load effects for fixed and floating wind turbines," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(1), January.
    2. Fan Zhang & Juchuan Dai & Deshun Liu & Linxing Li & Xin Long, 2019. "Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data," Energies, MDPI, vol. 12(3), pages 1-20, February.
    3. He, Guolin & Ding, Kang & Wu, Xiaomeng & Yang, Xiaoqing, 2019. "Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear," Renewable Energy, Elsevier, vol. 139(C), pages 718-729.
    4. Yunxuan Dong & Jianzhou Wang & Chen Wang & Zhenhai Guo, 2017. "Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting," Energies, MDPI, vol. 10(4), pages 1-27, April.
    5. Andrés Guggeri & Martín Draper, 2019. "Large Eddy Simulation of an Onshore Wind Farm with the Actuator Line Model Including Wind Turbine’s Control below and above Rated Wind Speed," Energies, MDPI, vol. 12(18), pages 1-21, September.
    6. He, Jiao & Jin, Xin & Xie, S.Y. & Cao, Le & Lin, Yifan & Wang, Ning, 2019. "Multi-body dynamics modeling and TMD optimization based on the improved AFSA for floating wind turbines," Renewable Energy, Elsevier, vol. 141(C), pages 305-321.
    7. Rodriguez, Steven N. & Jaworski, Justin W., 2019. "Strongly-coupled aeroelastic free-vortex wake framework for floating offshore wind turbine rotors. Part 1: Numerical framework," Renewable Energy, Elsevier, vol. 141(C), pages 1127-1145.

    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:13:y:2020:i:8:p:2108-:d:349553. 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.