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General Methodology for the Identification of Reduced Dynamic Models of Barge-Type Floating Wind Turbines

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  • Daniel Villoslada

    (Computer Sciences Faculty, University Complutense of Madrid, 28040 Madrid, Spain)

  • Matilde Santos

    (Institute of Knowledge Technology, University Complutense of Madrid, 28040 Madrid, Spain)

  • María Tomás-Rodríguez

    (Department of Mechanical Engineering and Aeronautics, School of Mathematics, Computer Science and Engineering, City University of London, London EC1V 0HB, UK)

Abstract

Floating offshore wind turbines (FOWT) are designed to overcome some of the limitations of offshore bottom-fixed ones. The development of computational models to simulate the behavior of the structure and the turbine is key to understanding the wind energy system and demonstrating its feasibility. In this work, a general methodology for the identification of reduced dynamic models of barge-type FOWTs is presented. The method is described together with an example of the development of a dynamic model of a 5 MW floating offshore wind turbine. The novelty of the proposed identification methodology lies in the iterative loop relationship between the identification and validation processes. Diversified data sets are used to select the best-fitting identified parameters by cross evaluation of every set among all validating conditions. The data set is generated for different initial FOWT operating conditions. Indeed, an optimal initial condition for platform pitch was found to be far enough from the system at rest to allow the dynamics to be well characterized but not so far that the unmodeled system nonlinearities were so large that they affected significantly the accuracy of the model. The model has been successfully applied to structural control research to reduce fatigue on a barge-type FOWT.

Suggested Citation

  • Daniel Villoslada & Matilde Santos & María Tomás-Rodríguez, 2021. "General Methodology for the Identification of Reduced Dynamic Models of Barge-Type Floating Wind Turbines," Energies, MDPI, vol. 14(13), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3902-:d:584465
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    References listed on IDEAS

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    1. J. Enrique Sierra-García & Matilde Santos, 2020. "Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning," Complexity, Hindawi, vol. 2020, pages 1-15, September.
    2. Esteban, M. Dolores & Diez, J. Javier & López, Jose S. & Negro, Vicente, 2011. "Why offshore wind energy?," Renewable Energy, Elsevier, vol. 36(2), pages 444-450.
    3. Yang Zhou & Qing Xiao & Yuanchuan Liu & Atilla Incecik & Christophe Peyrard & Sunwei Li & Guang Pan, 2019. "Numerical Modelling of Dynamic Responses of a Floating Offshore Wind Turbine Subject to Focused Waves," Energies, MDPI, vol. 12(18), pages 1-31, September.
    4. Yang, J.J. & He, E.M., 2020. "Coupled modeling and structural vibration control for floating offshore wind turbine," Renewable Energy, Elsevier, vol. 157(C), pages 678-694.
    5. Jingchun Chu & Ling Yuan & Yang Hu & Chenyang Pan & Lei Pan, 2019. "Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine," Energies, MDPI, vol. 12(18), pages 1-24, September.
    6. Kaldellis, J.K. & Kapsali, M., 2013. "Shifting towards offshore wind energy—Recent activity and future development," Energy Policy, Elsevier, vol. 53(C), pages 136-148.
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    Cited by:

    1. Matilde Santos, 2022. "Special Issue on Dynamics and Control of Offshore and Onshore Wind Turbine Structures," Energies, MDPI, vol. 15(8), pages 1-3, April.
    2. Kwangtae Ha & Jun-Bae Kim & Youngjae Yu & Hyoung-Seock Seo, 2021. "Structural Modeling and Failure Assessment of Spar-Type Substructure for 5 MW Floating Offshore Wind Turbine under Extreme Conditions in the East Sea," Energies, MDPI, vol. 14(20), pages 1-23, October.

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