IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v180y2018icp77-87.html
   My bibliography  Save this article

Towards analysing risks to public safety from wind turbines

Author

Listed:
  • Brouwer, Sander R.
  • Al-Jibouri, Saad H.S.
  • Cárdenas, Ibsen Chivatá
  • Halman, Johannes I.M.

Abstract

Wind energy has become an increasingly desirable and viable renewable energy source in recent years. However, wind energy faces a number of challenges, one of them being risks to public safety from wind turbine failures. This paper provides an analysis as a first step towards integrating wind turbine failures with public safety risks. In this paper, an existing Fault Tree Analysis (FTA) of wind turbines is expanded to include wind turbine failures that could be linked to public safety risks. The paper combines knowledge from literature related to wind turbine failures with expert judgements. Quantification of component failures and failure modes in the expanded FTA is carried out, and wind turbine failure modes related to the assessment of risks to public safety from wind turbines are analysed. The failures modes used in the Dutch system for assessing public safety risks from wind turbines are compared with the outcomes of this study and improvements to this assessment procedure are proposed. The paper concludes that the information available about wind turbine failures is still limited and there is a lack of detailed descriptions of incidents in the recorded data.

Suggested Citation

  • Brouwer, Sander R. & Al-Jibouri, Saad H.S. & Cárdenas, Ibsen Chivatá & Halman, Johannes I.M., 2018. "Towards analysing risks to public safety from wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 77-87.
  • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:77-87
    DOI: 10.1016/j.ress.2018.07.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832016307694
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2018.07.010?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. Sansavini, G. & Piccinelli, R. & Golea, L.R. & Zio, E., 2014. "A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation," Renewable Energy, Elsevier, vol. 64(C), pages 71-81.
    2. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    3. Refaul Ferdous & Faisal Khan & Rehan Sadiq & Paul Amyotte & Brian Veitch, 2011. "Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations," Risk Analysis, John Wiley & Sons, vol. 31(1), pages 86-107, January.
    4. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    5. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    6. Ibsen Chivatá Cárdenas & Saad S.H. Al‐jibouri & Johannes I.M. Halman & Frits A. van Tol, 2013. "Capturing and Integrating Knowledge for Managing Risks in Tunnel Works," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 92-108, January.
    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. Liu, Min & Qin, Jianjun & Lu, Da-Gang & Zhang, Wei-Heng & Zhu, Jiang-Sheng & Faber, Michael Havbro, 2022. "Towards resilience of offshore wind farms: A framework and application to asset integrity management," Applied Energy, Elsevier, vol. 322(C).
    2. Yang, Shenhao & Chen, Weirong & Zhang, Xuexia & Yang, Weiqi, 2021. "A Graph-based Method for Vulnerability Analysis of Renewable Energy integrated Power Systems to Cascading Failures," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    3. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    4. Gürdal Ertek & Lakshmi Kailas, 2021. "Analyzing a Decade of Wind Turbine Accident News with Topic Modeling," Sustainability, MDPI, vol. 13(22), pages 1-34, November.
    5. Sun, Wei & Lin, Wei-Cheng & You, Fei & Shu, Chi-Min & Qin, Sheng-Hui, 2019. "Prevention of green energy loss: Estimation of fire hazard potential in wind turbines," Renewable Energy, Elsevier, vol. 140(C), pages 62-69.

    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. Tzu Yang Loh & Mario P. Brito & Neil Bose & Jingjing Xu & Kiril Tenekedjiev, 2019. "A Fuzzy‐Based Risk Assessment Framework for Autonomous Underwater Vehicle Under‐Ice Missions," Risk Analysis, John Wiley & Sons, vol. 39(12), pages 2744-2765, December.
    2. Zhu, Yueying & Wang, Qiuping Alexandre & Li, Wei & Cai, Xu, 2017. "An analytic method for sensitivity analysis of complex systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 52-59.
    3. Crispim, José & Fernandes, Jorge & Rego, Nazaré, 2020. "Customized risk assessment in military shipbuilding," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    4. Ibsen Chivatá Cárdenas & Saad S. H. Al‐Jibouri & Johannes I. M. Halman & Wim van de Linde & Frank Kaalberg, 2014. "Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project," Risk Analysis, John Wiley & Sons, vol. 34(10), pages 1923-1943, October.
    5. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    6. Prianto Budi Saptono & Gustofan Mahmud & Intan Pratiwi & Dwi Purwanto & Ismail Khozen & Muhamad Akbar Aditama & Siti Khodijah & Maria Eurelia Wayan & Rina Yuliastuty Asmara & Ferry Jie, 2023. "Development of Climate-Related Disclosure Indicators for Application in Indonesia: A Delphi Method Study," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    7. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    8. Lin, Tun & De Guzman, Franklin, 2007. "Tourism for pro-poor and sustainable growth: economic analysis of tourism projects," MPRA Paper 24994, University Library of Munich, Germany.
    9. F. Wang & G. H. Huang & Y. Fan & Y. P. Li, 2020. "Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3199-3217, August.
    10. Di Zio, Simone & Bolzan, Mario & Marozzi, Marco, 2021. "Classification of Delphi outputs through robust ranking and fuzzy clustering for Delphi-based scenarios," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    11. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    12. Daniel Harenberg & Stefano Marelli & Bruno Sudret & Viktor Winschel, 2019. "Uncertainty quantification and global sensitivity analysis for economic models," Quantitative Economics, Econometric Society, vol. 10(1), pages 1-41, January.
    13. Alyami, Saleh. H. & Rezgui, Yacine & Kwan, Alan, 2013. "Developing sustainable building assessment scheme for Saudi Arabia: Delphi consultation approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 43-54.
    14. Ngoy Kabemba S. & Chisumbe Sampa & Petere Gaida & Mwiya Balimu & Mwanaumo Erastus, 2023. "Factors Influencing Professional Indemnity Insurance Use in Construction Risk Management," Baltic Journal of Real Estate Economics and Construction Management, Sciendo, vol. 11(1), pages 199-220, January.
    15. Frederico Fernandes Ávila & Regina C. Alvalá & Rodolfo M. Mendes & Diogo J. Amore, 2024. "Socio-geoenvironmental vulnerability index (SGeoVI) derived from hybrid modeling related to populations at-risk to landslides," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(9), pages 8121-8151, July.
    16. Magni, Carlo Alberto, 2016. "Capital depreciation and the underdetermination of rate of return: A unifying perspective," Journal of Mathematical Economics, Elsevier, vol. 67(C), pages 54-79.
    17. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    18. Matteo Fontana & Massimo Tavoni & Simone Vantini, 2020. "Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models," Papers 2006.13850, arXiv.org, revised Apr 2024.
    19. van Asselt, E.D. & Meuwissen, M.P.M. & van Asseldonk, M.A.P.M. & Sterrenburg, P. & Mengelers, M.J.B. & van der Fels-Klerx, H.J., 2011. "Approach for a pro-active emerging risk system on biofuel by-products in feed," Food Policy, Elsevier, vol. 36(3), pages 421-429, June.
    20. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.

    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:180:y:2018:i:c:p:77-87. 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.