IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5859-d636697.html
   My bibliography  Save this article

Reliability Updating of Offshore Wind Substructures by Use of Digital Twin Information

Author

Listed:
  • Dawid Augustyn

    (Department of the Built Environment, Aalborg University, 9220 Aalborg, Denmark
    Ramboll Energy, 6700 Esbjerg, Denmark)

  • Martin D. Ulriksen

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • John D. Sørensen

    (Department of the Built Environment, Aalborg University, 9220 Aalborg, Denmark)

Abstract

This paper presents a probabilistic framework for updating the structural reliability of offshore wind turbine substructures based on digital twin information. In particular, the information obtained from digital twins is used to quantify and update the uncertainties associated with the structural dynamics and load modeling parameters in fatigue damage accumulation. The updated uncertainties are included in a probabilistic model for fatigue damage accumulation used to update the structural reliability. The updated reliability can be used as input to optimize decision models for operation and maintenance of existing structures and design of new structures. The framework is exemplified based on two numerical case studies with a representative offshore wind turbine and information acquired from previously established digital twins. In this context, the effect of updating soil stiffness and wave loading, which constitute two highly uncertain and sensitive parameters, is investigated. It is found that updating the soil stiffness significantly affects the reliability of the joints close to the mudline, while updating the wave loading significantly affects the reliability of the joints localized in the splash zone. The increased uncertainty related to virtual sensing, which is employed to update wave loading, reduces structural reliability.

Suggested Citation

  • Dawid Augustyn & Martin D. Ulriksen & John D. Sørensen, 2021. "Reliability Updating of Offshore Wind Substructures by Use of Digital Twin Information," Energies, MDPI, vol. 14(18), pages 1-23, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5859-:d:636697
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5859/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/18/5859/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nielsen, Jannie Jessen & Sørensen, John Dalsgaard, 2011. "On risk-based operation and maintenance of offshore wind turbine components," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 218-229.
    2. Jannie S. Nielsen & John D. Sørensen, 2017. "Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades," Energies, MDPI, vol. 10(5), pages 1-13, May.
    3. Hevia-Koch, Pablo & Klinge Jacobsen, Henrik, 2019. "Comparing offshore and onshore wind development considering acceptance costs," Energy Policy, Elsevier, vol. 125(C), pages 9-19.
    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. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    2. Eirini Katsidoniotaki & Foivos Psarommatis & Malin Göteman, 2022. "Digital Twin for the Prediction of Extreme Loads on a Wave Energy Conversion System," Energies, MDPI, vol. 15(15), pages 1-24, July.
    3. Jannie Sønderkær Nielsen & Henrik Stensgaard Toft & Gustavo Oliveira Violato, 2023. "Risk-Based Assessment of the Reliability Level for Extreme Limit States in IEC 61400-1," Energies, MDPI, vol. 16(4), pages 1-15, February.

    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. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    2. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Masoud Asgarpour & John Dalsgaard Sørensen, 2018. "Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms," Energies, MDPI, vol. 11(2), pages 1-17, January.
    4. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Jin, Xin & Zhang, Zhaolong & Shi, Xiaoqiang & Ju, Wenbin, 2014. "A review on wind power industry and corresponding insurance market in China: Current status and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 1069-1082.
    6. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    7. Li, Yan-Fu & Zio, Enrico, 2012. "A multi-state model for the reliability assessment of a distributed generation system via universal generating function," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 28-36.
    8. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    9. Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).
    10. Leimeister, Mareike & Kolios, Athanasios, 2018. "A review of reliability-based methods for risk analysis and their application in the offshore wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1065-1076.
    11. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    12. Dibaj, Ali & Gao, Zhen & Nejad, Amir R., 2023. "Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements," Renewable Energy, Elsevier, vol. 203(C), pages 161-176.
    13. Qiu, Yue & Zhou, Suyang & Wang, Jihua & Chou, Jun & Fang, Yunhui & Pan, Guangsheng & Gu, Wei, 2020. "Feasibility analysis of utilising underground hydrogen storage facilities in integrated energy system: Case studies in China," Applied Energy, Elsevier, vol. 269(C).
    14. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    15. Soua, Slim & Van Lieshout, Paul & Perera, Asanka & Gan, Tat-Hean & Bridge, Bryan, 2013. "Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring," Renewable Energy, Elsevier, vol. 51(C), pages 175-181.
    16. Van Horenbeek, Adriaan & Van Ostaeyen, Joris & Duflou, Joost R. & Pintelon, Liliane, 2013. "Quantifying the added value of an imperfectly performing condition monitoring system—Application to a wind turbine gearbox," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 45-57.
    17. Lundin, Erik, 2024. "Wind power and the cost of local compensation schemes: A Swedish revenue sharing policy simulation," Energy Economics, Elsevier, vol. 135(C).
    18. Jannie Sønderkær Nielsen & Lindsay Miller-Branovacki & Rupp Carriveau, 2021. "Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components," Energies, MDPI, vol. 14(4), pages 1-16, February.
    19. Yeter, B. & Garbatov, Y. & Guedes Soares, C., 2020. "Risk-based maintenance planning of offshore wind turbine farms," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    20. Yürüşen, Nurseda Y. & Rowley, Paul N. & Watson, Simon J. & Melero, Julio J., 2020. "Automated wind turbine maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 200(C).

    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:14:y:2021:i:18:p:5859-:d:636697. 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.