IDEAS home Printed from https://ideas.repec.org/a/ibn/ijbmjn/v19y2024i3p1.html
   My bibliography  Save this article

Optimization Models for Operations and Maintenance of Offshore Wind Turbines Based on Artificial Intelligence and Operations Research: A Systematic Literature Review

Author

Listed:
  • Eric Lucas dos Santos Cabral
  • Mario Orestes Aguirre Gonzalez
  • Priscila da Cunha Jacome Vidal
  • Joao Florencio da Costa Junior
  • Rafael Monteiro de Vasconcelos
  • David Cassimiro de Melo
  • Ruan Lucas Leite de Morais
  • Joao Agra Neto

Abstract

Maintenance of offshore wind turbines is critical for expanding wind energy production, yet it presents significant challenges due to harsh operational conditions. This issue, discussed extensively in Operations and Maintenance (O&M) periodicals, can hinder the economic viability of wind energy. With European and emerging markets planning large-scale wind energy production, optimizing installation and maintenance resources is crucial. Our research focuses on numerical techniques to inform maintenance strategies and decisions, addressing key discussion areas. Our methodology involves a systematic literature review of 122 scientific works, with descriptive and content analyses revealing insights into maintenance planning. Quantitative techniques, while studied separately, can enhance understanding of technical aspects in maintenance decision-making, provided their limitations are addressed. The research underscores the importance of considering various factors in offshore wind farm maintenance planning to align with planner objectives.

Suggested Citation

  • Eric Lucas dos Santos Cabral & Mario Orestes Aguirre Gonzalez & Priscila da Cunha Jacome Vidal & Joao Florencio da Costa Junior & Rafael Monteiro de Vasconcelos & David Cassimiro de Melo & Ruan Lucas , 2024. "Optimization Models for Operations and Maintenance of Offshore Wind Turbines Based on Artificial Intelligence and Operations Research: A Systematic Literature Review," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(3), pages 1-1, June.
    • Handle: RePEc:ibn:ijbmjn:v:19:y:2024:i:3:p:1
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/ijbm/article/download/0/0/50028/54129
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/ijbm/article/view/0/50028
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Boccard, Nicolas, 2009. "Capacity factor of wind power realized values vs. estimates," Energy Policy, Elsevier, vol. 37(7), pages 2679-2688, July.
    2. Fatih Camci, 2014. "The travelling maintainer problem: integration of condition-based maintenance with the travelling salesman problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(9), pages 1423-1436, September.
    3. Faye Duchin, 2005. "A world trade model based on comparative advantage with m regions, n goods, and k factors," Economic Systems Research, Taylor & Francis Journals, vol. 17(2), pages 141-162.
    4. Azadegan, Arash & Porobic, Lejla & Ghazinoory, Sepehr & Samouei, Parvaneh & Saman Kheirkhah, Amir, 2011. "Fuzzy logic in manufacturing: A review of literature and a specialized application," International Journal of Production Economics, Elsevier, vol. 132(2), pages 258-270, August.
    5. Bach, Lukas & Hasle, Geir & Schulz, Christian, 2019. "Adaptive Large Neighborhood Search on the Graphics Processing Unit," European Journal of Operational Research, Elsevier, vol. 275(1), pages 53-66.
    6. Ding, Fangfang & Tian, Zhigang & Zhao, Fuqiong & Xu, Hao, 2018. "An integrated approach for wind turbine gearbox fatigue life prediction considering instantaneously varying load conditions," Renewable Energy, Elsevier, vol. 129(PA), pages 260-270.
    7. Altman, Rachel MacKay, 2007. "Mixed Hidden Markov Models: An Extension of the Hidden Markov Model to the Longitudinal Data Setting," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 201-210, March.
    8. Ahsan, Dewan & Pedersen, Søren, 2018. "The influence of stakeholder groups in operation and maintenance services of offshore wind farms: Lesson from Denmark," Renewable Energy, Elsevier, vol. 125(C), pages 819-828.
    9. David Applegate & William Cook & Sanjeeb Dash & André Rohe, 2002. "Solution of a Min-Max Vehicle Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 14(2), pages 132-143, May.
    10. Akbari, Negar & Irawan, Chandra A. & Jones, Dylan F. & Menachof, David, 2017. "A multi-criteria port suitability assessment for developments in the offshore wind industry," Renewable Energy, Elsevier, vol. 102(PA), pages 118-133.
    11. Alejandro Blanco-M. & Karina Gibert & Pere Marti-Puig & Jordi Cusidó & Jordi Solé-Casals, 2018. "Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools," Energies, MDPI, vol. 11(4), pages 1-21, March.
    Full references (including those not matched with items on IDEAS)

    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. Ioannidis, Romanos & Koutsoyiannis, Demetris, 2020. "A review of land use, visibility and public perception of renewable energy in the context of landscape impact," Applied Energy, Elsevier, vol. 276(C).
    2. Faye Duchin & Stephen Levine, 2012. "The rectangular sector-by-technology model: not every economy produces every product and some products may rely on several technologies simultaneously," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 1(1), pages 1-11, December.
    3. Scaringella, Laurent & Burtschell, François, 2017. "The challenges of radical innovation in Iran: Knowledge transfer and absorptive capacity highlights — Evidence from a joint venture in the construction sector," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 151-169.
    4. Spezia, L. & Cooksley, S.L. & Brewer, M.J. & Donnelly, D. & Tree, A., 2014. "Modelling species abundance in a river by Negative Binomial hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 599-614.
    5. Marino, Maria Francesca & Alfó, Marco, 2016. "Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 193-209.
    6. Haji Vahabzadeh, Ali & Asiaei, Arash & Zailani, Suhaiza, 2015. "Reprint of “Green decision-making model in reverse logistics using FUZZY-VIKOR method”," Resources, Conservation & Recycling, Elsevier, vol. 104(PB), pages 334-347.
    7. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    8. Marques, António Cardoso & Fuinhas, José Alberto & Menegaki, Angeliki N., 2014. "Interactions between electricity generation sources and economic activity in Greece: A VECM approach," Applied Energy, Elsevier, vol. 132(C), pages 34-46.
    9. Paul L. Joskow, 2011. "Comparing the Costs of Intermittent and Dispatchable Electricity Generating Technologies," American Economic Review, American Economic Association, vol. 101(3), pages 238-241, May.
    10. Boccard, Nicolas, 2010. "Economic properties of wind power: A European assessment," Energy Policy, Elsevier, vol. 38(7), pages 3232-3244, July.
    11. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    12. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 484-486, September.
    13. Havinga, Maik J.A. & de Jonge, Bram, 2020. "Condition-based maintenance in the cyclic patrolling repairman problem," International Journal of Production Economics, Elsevier, vol. 222(C).
    14. Linnell, Peter, 2010. "Are Smaller Turbines the Way Forward for Wind Energy in Herefordshire?," MPRA Paper 58879, University Library of Munich, Germany.
    15. Lo, Huai-Wei & Liou, James J.H. & Huang, Chun-Nen & Chuang, Yen-Ching & Tzeng, Gwo-Hshiung, 2020. "A new soft computing approach for analyzing the influential relationships of critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 28(C).
    16. Dilaver, Halit Metehan & Akçay, Alp & van Houtum, Geert-Jan, 2023. "Integrated planning of asset-use and dry-docking for a fleet of maritime assets," International Journal of Production Economics, Elsevier, vol. 256(C).
    17. Kagawa, Shigemi, 2008. "How does Japanese compliance with the Kyoto Protocol affect environmental productivity in China and Japan?," Structural Change and Economic Dynamics, Elsevier, vol. 19(2), pages 173-188, June.
    18. de Medeiros, Armando Lúcio Ramos & Araújo, Alex Maurício & de Oliveira Filho, Oyama Douglas Queiroz & Rohatgi, Janardan & dos Santos, Maurílio José, 2015. "Analysis of design parameters of large-sized wind turbines by non-dimensional model," Energy, Elsevier, vol. 93(P1), pages 1146-1154.
    19. Jonasson, Erik & Fjellstedt, Christoffer & Temiz, Irina, 2024. "Grid impact of co-located offshore renewable energy sources," Renewable Energy, Elsevier, vol. 230(C).
    20. Considine, Timothy J. & Manderson, Edward J.M., 2015. "The cost of solar-centric renewable portfolio standards and reducing coal power generation using Arizona as a case study," Energy Economics, Elsevier, vol. 49(C), pages 402-419.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:ibn:ijbmjn:v:19:y:2024:i:3:p:1. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.