IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v115y2013icp19-32.html
   My bibliography  Save this article

Offshore wind turbine risk quantification/evaluation under extreme environmental conditions

Author

Listed:
  • Taflanidis, Alexandros A.
  • Loukogeorgaki, Eva
  • Angelides, Demos C.

Abstract

A simulation-based framework is discussed in this paper for quantification/evaluation of risk and development of automated risk assessment tools, focusing on applications to offshore wind turbines under extreme environmental conditions. The framework is founded on a probabilistic characterization of the uncertainty in the models for the excitation, the turbine and its performance. Risk is then quantified as the expected value of some risk consequence measure over the probability distributions considered for the uncertain model parameters. Stochastic simulation is proposed for the risk assessment, corresponding to the evaluation of some associated probabilistic integral quantifying risk, as it allows for the adoption of comprehensive computational models for describing the dynamic turbine behavior. For improvement of the computational efficiency, a surrogate modeling approach is introduced based on moving least squares response surface approximations. The assessment is also extended to a probabilistic sensitivity analysis that identifies the importance of each of the uncertain model parameters, i.e. risk factors, towards the total risk as well as towards each of the failure modes contributing to this risk. The versatility and computational efficiency of the advocated approaches is finally exploited to support the development of standalone risk assessment applets for automated implementation of the probabilistic risk quantification/assessment.

Suggested Citation

  • Taflanidis, Alexandros A. & Loukogeorgaki, Eva & Angelides, Demos C., 2013. "Offshore wind turbine risk quantification/evaluation under extreme environmental conditions," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 19-32.
    • Handle: RePEc:eee:reensy:v:115:y:2013:i:c:p:19-32
    DOI: 10.1016/j.ress.2013.02.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832013000380
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2013.02.003?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. Na, Ung Jin & Shinozuka, Masanobu, 2009. "Simulation-based seismic loss estimation of seaport transportation system," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 722-731.
    2. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    3. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
    4. Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
    5. Karunamuni, R.J. & Zhang, S., 2008. "Some improvements on a boundary corrected kernel density estimator," Statistics & Probability Letters, Elsevier, vol. 78(5), pages 499-507, April.
    6. Leira, Bernt J. & Holmås, Tore & Herfjord, Kjell, 2005. "Application of response surfaces for reliability analysis of marine structures," Reliability Engineering and System Safety, Elsevier, vol. 90(2), pages 131-139.
    7. Breton, Simon-Philippe & Moe, Geir, 2009. "Status, plans and technologies for offshore wind turbines in Europe and North America," Renewable Energy, Elsevier, vol. 34(3), pages 646-654.
    8. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
    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. Maheri, Alireza, 2014. "A critical evaluation of deterministic methods in size optimisation of reliable and cost effective standalone hybrid renewable energy systems," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 159-174.
    2. Wei, K. & Arwade, S.R. & Myers, A.T. & Hallowell, S. & Hajjar, J.F. & Hines, E.M. & Pang, W., 2016. "Toward performance-based evaluation for offshore wind turbine jacket support structures," Renewable Energy, Elsevier, vol. 97(C), pages 709-721.
    3. 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.
    4. Pokhrel, Jharna & Seo, Junwon, 2021. "Statistical model for fragility estimates of offshore wind turbines subjected to aero-hydro dynamic loads," Renewable Energy, Elsevier, vol. 163(C), pages 1495-1507.
    5. Hong, H.P., 2013. "Selection of regressand for fitting the extreme value distributions using the ordinary, weighted and generalized least-squares methods," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 71-80.
    6. 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.
    7. Liu, Ding Peng & Ferri, Giulio & Heo, Taemin & Marino, Enzo & Manuel, Lance, 2024. "On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model," Renewable Energy, Elsevier, vol. 225(C).
    8. Lee, Yeon-Seung & Choi, Byung-Lyul & Lee, Ji Hyun & Kim, Soo Young & Han, Soonhung, 2014. "Reliability-based design optimization of monopile transition piece for offshore wind turbine system," Renewable Energy, Elsevier, vol. 71(C), pages 729-741.
    9. 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.
    10. Hu, Xiaonong & Fang, Genshen & Yang, Jiayu & Zhao, Lin & Ge, Yaojun, 2023. "Simplified models for uncertainty quantification of extreme events using Monte Carlo technique," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. Charlton, T.S. & Rouainia, M., 2022. "Geotechnical fragility analysis of monopile foundations for offshore wind turbines in extreme storms," Renewable Energy, Elsevier, vol. 182(C), pages 1126-1140.

    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. Wei, Pengfei & Liu, Fuchao & Tang, Chenghu, 2018. "Reliability and reliability-based importance analysis of structural systems using multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 183-195.
    2. Menz, Morgane & Gogu, Christian & Dubreuil, Sylvain & Bartoli, Nathalie & Morio, Jérôme, 2020. "Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    3. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.
    4. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    5. Keshtegar, Behrooz & Chakraborty, Subrata, 2018. "An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 195-206.
    6. Xu, Jun & Kong, Fan, 2018. "A new unequal-weighted sampling method for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 94-102.
    7. Pengfei Wei & Chenghu Tang & Yuting Yang, 2019. "Structural reliability and reliability sensitivity analysis of extremely rare failure events by combining sampling and surrogate model methods," Journal of Risk and Reliability, , vol. 233(6), pages 943-957, December.
    8. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    9. Yang, Seonghyeok & Lee, Mingyu & Lee, Ikjin, 2023. "A new sampling approach for system reliability-based design optimization under multiple simulation models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Fauriat, W. & Gayton, N., 2014. "AK-SYS: An adaptation of the AK-MCS method for system reliability," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 137-144.
    11. Yang, Seonghyeok & Jo, Hwisang & Lee, Kyungeun & Lee, Ikjin, 2022. "Expected system improvement (ESI): A new learning function for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    13. Guo, Qing & Liu, Yongshou & Chen, Bingqian & Yao, Qin, 2021. "A variable and mode sensitivity analysis method for structural system using a novel active learning Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    14. Chen, Zequan & He, Jialong & Li, Guofa & Yang, Zhaojun & Wang, Tianzhe & Du, Xuejiao, 2024. "Fast convergence strategy for adaptive structural reliability analysis based on kriging believer criterion and importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    15. Zhao, Wei & Fan, Feng & Wang, Wei, 2017. "Non-linear partial least squares response surface method for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 69-77.
    16. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2021. "A Kriging-assisted sampling method for reliability analysis of structures with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    17. Jian, Wang & Zhili, Sun & Qiang, Yang & Rui, Li, 2017. "Two accuracy measures of the Kriging model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 494-505.
    18. Ling, Chunyan & Lu, Zhenzhou & Zhu, Xianming, 2019. "Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 23-35.
    19. Kroetz, H.M. & Moustapha, M. & Beck, A.T. & Sudret, B., 2020. "A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    20. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(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:eee:reensy:v:115:y:2013:i:c:p:19-32. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.