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System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling

Author

Listed:
  • Giorgio Bacelli

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Ryan G. Coe

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • David Patterson

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • David Wilson

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

Abstract

Empirically based modeling is an essential aspect of design for a wave energy converter. Empirically based models are used in structural, mechanical and control design processes, as well as for performance prediction. Both the design of experiments and methods used in system identification have a strong impact on the quality of the resulting model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages, including an increased signal-to-noise ratio, reduced experimental time and higher frequency resolution. The experimental wave tank data is used to produce multiple models using different formulations to represent the dynamics of the wave energy converter. These models are validated and their performance is compared against one another. While most models of wave energy converters use a formulation with surface elevation as an input, this study shows that a model using a hull pressure measurement to incorporate the wave excitation phenomenon has better accuracy.

Suggested Citation

  • Giorgio Bacelli & Ryan G. Coe & David Patterson & David Wilson, 2017. "System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling," Energies, MDPI, vol. 10(4), pages 1-33, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:472-:d:94864
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    Citations

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    Cited by:

    1. Pablo Ropero-Giralda & Alejandro J. C. Crespo & Ryan G. Coe & Bonaventura Tagliafierro & José M. Domínguez & Giorgio Bacelli & Moncho Gómez-Gesteira, 2021. "Modelling a Heaving Point-Absorber with a Closed-Loop Control System Using the DualSPHysics Code," Energies, MDPI, vol. 14(3), pages 1-20, February.
    2. Eva Segura & Rafael Morales & José A. Somolinos, 2017. "Cost Assessment Methodology and Economic Viability of Tidal Energy Projects," Energies, MDPI, vol. 10(11), pages 1-27, November.
    3. Ryan G. Coe & Yi-Hsiang Yu & Jennifer Van Rij, 2017. "A Survey of WEC Reliability, Survival and Design Practices," Energies, MDPI, vol. 11(1), pages 1-19, December.
    4. Fernando Jaramillo-Lopez & Brian Flannery & Jimmy Murphy & John V. Ringwood, 2020. "Modelling of a Three-Body Hinge-Barge Wave Energy Device Using System Identification Techniques," Energies, MDPI, vol. 13(19), pages 1-16, October.
    5. Forbush, Dominic D. & Bacelli, Giorgio & Spencer, Steven J. & Coe, Ryan G. & Bosma, Bret & Lomonaco, Pedro, 2022. "Design and testing of a free floating dual flap wave energy converter," Energy, Elsevier, vol. 240(C).
    6. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    7. Meng, Fantai & Rafiee, Ashkan & Ding, Boyin & Cazzolato, Benjamin & Arjomandi, Maziar, 2020. "Nonlinear hydrodynamics analysis of a submerged spherical point absorber with asymmetric mass distribution," Renewable Energy, Elsevier, vol. 147(P1), pages 1895-1908.
    8. Coe, Ryan G. & Bacelli, Giorgio & Forbush, Dominic, 2021. "A practical approach to wave energy modeling and control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    9. Gianmaria Giannini & Paulo Rosa-Santos & Victor Ramos & Francisco Taveira-Pinto, 2020. "On the Development of an Offshore Version of the CECO Wave Energy Converter," Energies, MDPI, vol. 13(5), pages 1-24, February.
    10. Hancheol Cho & Giorgio Bacelli & Ryan G. Coe, 2019. "Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter," Energies, MDPI, vol. 12(21), pages 1-18, October.
    11. Tagliafierro, Bonaventura & Martínez-Estévez, Iván & Domínguez, José M. & Crespo, Alejandro J.C. & Göteman, Malin & Engström, Jens & Gómez-Gesteira, Moncho, 2022. "A numerical study of a taut-moored point-absorber wave energy converter with a linear power take-off system under extreme wave conditions," Applied Energy, Elsevier, vol. 311(C).
    12. Guo, Bingyong & Patton, Ron J. & Jin, Siya & Lan, Jianglin, 2018. "Numerical and experimental studies of excitation force approximation for wave energy conversion," Renewable Energy, Elsevier, vol. 125(C), pages 877-889.

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