IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4439-d599622.html
   My bibliography  Save this article

Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks

Author

Listed:
  • Albara M. Mustafa

    (Department of Technology and Safety, UiT The Arctic University of Norway, 6050 Tromsø, Norway)

  • Abbas Barabadi

    (Department of Technology and Safety, UiT The Arctic University of Norway, 6050 Tromsø, Norway)

Abstract

Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions.

Suggested Citation

  • Albara M. Mustafa & Abbas Barabadi, 2021. "Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks," Energies, MDPI, vol. 14(15), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4439-:d:599622
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4439/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4439/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Morris, Peter & Vine, Desley & Buys, Laurie, 2015. "Application of a Bayesian Network complex system model to a successful community electricity demand reduction program," Energy, Elsevier, vol. 84(C), pages 63-74.
    2. Barabadi, A. & Ayele, Y.Z., 2018. "Post-disaster infrastructure recovery: Prediction of recovery rate using historical data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 209-223.
    3. Kwok L. Tsui & Nan Chen & Qiang Zhou & Yizhen Hai & Wenbin Wang, 2015. "Prognostics and Health Management: A Review on Data Driven Approaches," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-17, May.
    4. Kabir, Golam & Tesfamariam, Solomon & Francisque, Alex & Sadiq, Rehan, 2015. "Evaluating risk of water mains failure using a Bayesian belief network model," European Journal of Operational Research, Elsevier, vol. 240(1), pages 220-234.
    5. Terje Aven, 2019. "The Call for a Shift from Risk to Resilience: What Does it Mean?," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1196-1203, June.
    6. Aven, Terje & Krohn, Bodil S., 2014. "A new perspective on how to understand, assess and manage risk and the unforeseen," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 1-10.
    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. Albara M. Mustafa & Abbas Barabadi, 2022. "Criteria-Based Fuzzy Logic Risk Analysis of Wind Farms Operation in Cold Climate Regions," Energies, MDPI, vol. 15(4), pages 1-17, February.
    2. Ziyi Wang & Zengqiao Chen & Cuiping Ma & Ronald Wennersten & Qie Sun, 2022. "Nationwide Evaluation of Urban Energy System Resilience in China Using a Comprehensive Index Method," Sustainability, MDPI, vol. 14(4), pages 1-36, February.
    3. Kirill A. Bashmur & Oleg A. Kolenchukov & Vladimir V. Bukhtoyarov & Vadim S. Tynchenko & Sergei O. Kurashkin & Elena V. Tsygankova & Vladislav V. Kukartsev & Roman B. Sergienko, 2022. "Biofuel Technologies and Petroleum Industry: Synergy of Sustainable Development for the Eastern Siberian Arctic," Sustainability, MDPI, vol. 14(20), pages 1-25, October.

    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. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Charles Sabel & Gary Herrigel & Peer Hull Kristensen, 2018. "Regulation under uncertainty: The coevolution of industry and regulation," Regulation & Governance, John Wiley & Sons, vol. 12(3), pages 371-394, September.
    3. Sven Ove Hansson & Terje Aven, 2014. "Is Risk Analysis Scientific?," Risk Analysis, John Wiley & Sons, vol. 34(7), pages 1173-1183, July.
    4. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
    5. De Iuliis, Melissa & Kammouh, Omar & Cimellaro, Gian Paolo & Tesfamariam, Solomon, 2021. "Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Bjerga, Torbjørn & Aven, Terje, 2015. "Adaptive risk management using new risk perspectives – an example from the oil and gas industry," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 75-82.
    7. Terje Aven & Ortwin Renn, 2015. "An Evaluation of the Treatment of Risk and Uncertainties in the IPCC Reports on Climate Change," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 701-712, April.
    8. Ali Rohan, 2022. "Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    9. Iman Moslehi & Mohammadreza Jalili_Ghazizadeh, 2020. "Pressure-Pipe Breaks Relationship in Water Distribution Networks: A Statistical Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2851-2868, July.
    10. Mrinal Kanti Sen & Subhrajit Dutta & Golam Kabir, 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    11. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    12. Kaya, Gulsum Kubra & Hocaoglu, Mehmet Fatih, 2020. "Semi-quantitative application to the Functional Resonance Analysis Method for supporting safety management in a complex health-care process," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    13. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.
    14. Zio, Enrico, 2016. "Challenges in the vulnerability and risk analysis of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 137-150.
    15. Bae, Jinwoo & Xi, Zhimin, 2022. "Learning of physical health timestep using the LSTM network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. Ma, Jie & Cai, Li & Liao, Guobo & Yin, Hongpeng & Si, Xiaosheng & Zhang, Peng, 2023. "A multi-phase Wiener process-based degradation model with imperfect maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    17. Rego, Erik Eduardo & Costa, Oswaldo L.V. & Ribeiro, Celma de Oliveira & Lima Filho, Roberto Ivo da R. & Takada, Hellinton & Stern, Julio, 2020. "The trade-off between demand growth and renewables: A multiperiod electricity planning model under CO2 emission constraints," Energy, Elsevier, vol. 213(C).
    18. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    19. Grego, Marica & Magnani, Giovanna & Denicolai, Stefano, 2024. "Transform to adapt or resilient by design? How organizations can foster resilience through business model transformation," Journal of Business Research, Elsevier, vol. 171(C).
    20. Ghaneshvar Ramineni & Nafiseh Ghorbani-Renani & Kash Barker & Andrés D. González & Talayeh Razzaghi & Sridhar Radhakrishnan, 2023. "Machine learning approaches to modeling interdependent network restoration time," Environment Systems and Decisions, Springer, vol. 43(1), pages 22-35, March.

    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:15:p:4439-:d:599622. 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.