IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5616-d1202707.html
   My bibliography  Save this article

Geospatial Analysis of Scour in Offshore Wind Farms

Author

Listed:
  • Clara Matutano Molina

    (Grupo de Investigación ARIES, Universidad Antonio de Nebrija, C. de Sta. Cruz de Marcenado, 27, 28015 Madrid, Spain)

  • Christian Velasco-Gallego

    (Grupo de Investigación ARIES, Universidad Antonio de Nebrija, C. de Sta. Cruz de Marcenado, 27, 28015 Madrid, Spain)

  • Nerea Portillo-Juan

    (Universidad Politécnica de Madrid, Campus Ciudad Universitaria, Calle del Profesor Aranguren 3, 28040 Madrid, Spain)

  • Vicente Negro Valdecantos

    (Universidad Politécnica de Madrid, Campus Ciudad Universitaria, Calle del Profesor Aranguren 3, 28040 Madrid, Spain)

  • Nieves Cubo-Mateo

    (Grupo de Investigación ARIES, Universidad Antonio de Nebrija, C. de Sta. Cruz de Marcenado, 27, 28015 Madrid, Spain)

Abstract

Climate change has highlighted the need to promote renewable energies. The offshore wind industry is growing exponentially because of some political strategies supported by various organizations, such as the European Union. The implementation of these strategies is commonly associated with significant investments, public acceptance, or achieving better installations and greater cumulative capacities. To ensure that offshore renewable energy projects could reach their ambitious targets, this study promotes the implementation of political strategies or planning decisions using data mining techniques and analytical tools. Strategic decisions based on real data analysis could help to achieve more suitable and optimal infrastructures. The scour phenomenon jeopardizes the operability of offshore wind farms, making it necessary to study its evolution over the years. In this work, extensive research on the scour phenomenon in offshore wind farms using real data (from the Lynn and Inner Dowsing offshore wind farms located in the UK) was performed, which revealed an evident lack of consideration of this phenomenon for data-driven decision-making processes. As a novelty, this research develops a detailed geospatial analysis of data, studying the possible autocorrelation of scour data measured from each turbine between 2011 and 2015. The conclusions obtained could be used to improve future planning tasks in offshore wind farms.

Suggested Citation

  • Clara Matutano Molina & Christian Velasco-Gallego & Nerea Portillo-Juan & Vicente Negro Valdecantos & Nieves Cubo-Mateo, 2023. "Geospatial Analysis of Scour in Offshore Wind Farms," Energies, MDPI, vol. 16(15), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5616-:d:1202707
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5616/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5616/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richmond, M. & Sobey, A. & Pandit, R. & Kolios, A., 2020. "Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning," Renewable Energy, Elsevier, vol. 161(C), pages 650-661.
    2. Hugo Díaz & C. Guedes Soares, 2022. "Multicriteria Decision Approach to the Design of Floating Wind Farm Export Cables," Energies, MDPI, vol. 15(18), pages 1-18, September.
    3. Matutano, Clara & Negro, Vicente & López-Gutiérrez, Jose-Santos & Esteban, M. Dolores, 2013. "Scour prediction and scour protections in offshore wind farms," Renewable Energy, Elsevier, vol. 57(C), pages 358-365.
    4. Yin, Xiuxing & Zhang, Wencan & Jiang, Zhansi & Pan, Li, 2020. "Data-driven multi-objective predictive control of offshore wind farm based on evolutionary optimization," Renewable Energy, Elsevier, vol. 160(C), pages 974-986.
    5. Carlos Emilio Arboleda Chavez & Vasiliki Stratigaki & Minghao Wu & Peter Troch & Alexander Schendel & Mario Welzel & Raúl Villanueva & Torsten Schlurmann & Leen De Vos & Dogan Kisacik & Francisco Tave, 2019. "Large-Scale Experiments to Improve Monopile Scour Protection Design Adapted to Climate Change—The PROTEUS Project," Energies, MDPI, vol. 12(9), pages 1-25, May.
    6. Chaouachi, Aymen & Covrig, Catalin Felix & Ardelean, Mircea, 2017. "Multi-criteria selection of offshore wind farms: Case study for the Baltic States," Energy Policy, Elsevier, vol. 103(C), pages 179-192.
    7. Majidi Nezhad, M. & Heydari, A. & Pirshayan, E. & Groppi, D. & Astiaso Garcia, D., 2021. "A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method," Renewable Energy, Elsevier, vol. 179(C), pages 2198-2211.
    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. Dapeng Zhang & Yunsheng Ma & Huiling Zhang & Yi Zhang, 2024. "Marine Equipment Siting Using Machine-Learning-Based Ocean Remote Sensing Data: Current Status and Future Prospects," Sustainability, MDPI, vol. 16(20), pages 1-26, 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. Escobar, A. & Negro, V. & López-Gutiérrez, J.S. & Esteban, M.D., 2019. "Assessment of the influence of the acceleration field on scour phenomenon in offshore wind farms," Renewable Energy, Elsevier, vol. 136(C), pages 1036-1043.
    2. Wenhui Wei & Abdollah Malekjafarian & M. Salauddin, 2024. "Scour Protection Measures for Offshore Wind Turbines: A Systematic Literature Review on Recent Developments," Energies, MDPI, vol. 17(5), pages 1-28, February.
    3. Deveci, Muhammet & Cali, Umit & Kucuksari, Sadik & Erdogan, Nuh, 2020. "Interval type-2 fuzzy sets based multi-criteria decision-making model for offshore wind farm development in Ireland," Energy, Elsevier, vol. 198(C).
    4. Dong, Zhen & Li, Zhongguo & Liang, Zhongchao & Xu, Yiqiao & Ding, Zhengtao, 2021. "Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion," Applied Energy, Elsevier, vol. 303(C).
    5. Hugo Díaz & Carlos Guedes Soares, 2021. "A Multi-Criteria Approach to Evaluate Floating Offshore Wind Farms Siting in the Canary Islands (Spain)," Energies, MDPI, vol. 14(4), pages 1-18, February.
    6. Luengo, Jorge & Negro, Vicente & García-Barba, Javier & López-Gutiérrez, José-Santos & Esteban, M. Dolores, 2019. "New detected uncertainties in the design of foundations for offshore Wind Turbines," Renewable Energy, Elsevier, vol. 131(C), pages 667-677.
    7. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    8. Virtanen, E.A. & Lappalainen, J. & Nurmi, M. & Viitasalo, M. & Tikanmäki, M. & Heinonen, J. & Atlaskin, E. & Kallasvuo, M. & Tikkanen, H. & Moilanen, A., 2022. "Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    9. Arkadiusz Dobrzycki & Jacek Roman, 2022. "Correlation between the Production of Electricity by Offshore Wind Farms and the Demand for Electricity in Polish Conditions," Energies, MDPI, vol. 15(10), pages 1-18, May.
    10. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    11. Paweł Ziemba & Jarosław Wątróbski & Magdalena Zioło & Artur Karczmarczyk, 2017. "Using the PROSA Method in Offshore Wind Farm Location Problems," Energies, MDPI, vol. 10(11), pages 1-20, November.
    12. Ziemba, Paweł, 2022. "Uncertain Multi-Criteria analysis of offshore wind farms projects investments – Case study of the Polish Economic Zone of the Baltic Sea," Applied Energy, Elsevier, vol. 309(C).
    13. Mingyu Li & Dongxiao Niu & Zhengsen Ji & Xiwen Cui & Lijie Sun, 2021. "Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net," Sustainability, MDPI, vol. 13(21), pages 1-19, November.
    14. Jessica Weber & Johann Köppel, 2022. "Can MCDA Serve Ex-Post to Indicate ‘Winners and Losers’ in Sustainability Dilemmas? A Case Study of Marine Spatial Planning in Germany," Energies, MDPI, vol. 15(20), pages 1-30, October.
    15. Gherboudj, Imen & Zorgati, Mohamed & Chamarthi, Phani-Kumar & Tuomiranta, Arttu & Mohandes, Baraa & Beegum, Naseema S. & Al-Sudairi, Jood & Al-Owain, Omar & Shibli, Hussain & El-Moursi, Mohamed & Ghed, 2021. "Renewable energy management system for Saudi Arabia: Methodology and preliminary results," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    16. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2018. "A lifecycle techno-economic model of offshore wind energy for different entry and exit instances," Applied Energy, Elsevier, vol. 221(C), pages 406-424.
    17. Lorenzo Cottura & Riccardo Caradonna & Alberto Ghigo & Riccardo Novo & Giovanni Bracco & Giuliana Mattiazzo, 2021. "Dynamic Modeling of an Offshore Floating Wind Turbine for Application in the Mediterranean Sea," Energies, MDPI, vol. 14(1), pages 1-34, January.
    18. Charakopoulos, Avraam & Karakasidis, Theodoros & Sarris, loannis, 2019. "Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis," Energy Policy, Elsevier, vol. 133(C).
    19. Sofia Spyridonidou & Georgia Sismani & Eva Loukogeorgaki & Dimitra G. Vagiona & Hagit Ulanovsky & Daniel Madar, 2021. "Sustainable Spatial Energy Planning of Large-Scale Wind and PV Farms in Israel: A Collaborative and Participatory Planning Approach," Energies, MDPI, vol. 14(3), pages 1-23, January.
    20. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.

    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:16:y:2023:i:15:p:5616-:d:1202707. 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.