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Geospatial Analysis of Scour in Offshore Wind Farms

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  • 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
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    References listed on IDEAS

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    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. 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.
    4. 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.
    5. 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.
    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.
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