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Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays

Author

Listed:
  • Simon Thomas

    (Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden)

  • Mikael Eriksson

    (Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden)

  • Malin Göteman

    (Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden)

  • Martyn Hann

    (School of Engineering, University of Plymouth, Drake Circuit, Plymouth PL4 8AA, UK)

  • Jan Isberg

    (Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden)

  • Jens Engström

    (Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden)

Abstract

A challenge while applying latching control on a wave energy converter (WEC) is to find a reliable and robust control strategy working in irregular waves and handling the non-ideal behavior of real WECs. In this paper, a robust and model-free collaborative learning approach for latchable WECs in an array is presented. A machine learning algorithm with a shallow artificial neural network (ANN) is used to find optimal latching times. The applied strategy is compared to a latching time that is linearly correlated with the mean wave period: It is remarkable that the ANN-based WEC achieved a similar power absorption as the WEC applying a linear latching time, by applying only two different latching times. The strategy was tested in a numerical simulation, where for some sea states it absorbed more than twice the power compared to the uncontrolled WEC and over 30% more power than a WEC with constant latching. In wave tank tests with a 1:10 physical scale model the advantage decreased to +3% compared to the best tested constant latching WEC, which is explained by the lower advantage of the latching strategy caused by the non-ideal latching of the physical power take-off model.

Suggested Citation

  • Simon Thomas & Mikael Eriksson & Malin Göteman & Martyn Hann & Jan Isberg & Jens Engström, 2018. "Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays," Energies, MDPI, vol. 11(11), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3036-:d:180660
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    References listed on IDEAS

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    1. Sánchez, Antonio Santos & Rodrigues, Diego Arruda & Fontes, Raony Maia & Martins, Márcio Fernandes & Kalid, Ricardo de Araújo & Torres, Ednildo Andrade, 2018. "Wave resource characterization through in-situ measurement followed by artificial neural networks' modeling," Renewable Energy, Elsevier, vol. 115(C), pages 1055-1066.
    2. Simon Thomas & Marianna Giassi & Malin Göteman & Martyn Hann & Edward Ransley & Jan Isberg & Jens Engström, 2018. "Performance of a Direct-Driven Wave Energy Point Absorber with High Inertia Rotatory Power Take-off," Energies, MDPI, vol. 11(9), pages 1-17, September.
    3. Chenhua Ni & Xiandong Ma, 2018. "Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs," Energies, MDPI, vol. 11(8), pages 1-18, August.
    4. Li, Guang & Belmont, Michael R., 2014. "Model predictive control of sea wave energy converters – Part I: A convex approach for the case of a single device," Renewable Energy, Elsevier, vol. 69(C), pages 453-463.
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    Cited by:

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    2. Hong-Wei Fang & Yu-Zhu Feng & Guo-Ping Li, 2018. "Optimization of Wave Energy Converter Arrays by an Improved Differential Evolution Algorithm," Energies, MDPI, vol. 11(12), pages 1-19, December.
    3. Eirini Katsidoniotaki & Foivos Psarommatis & Malin Göteman, 2022. "Digital Twin for the Prediction of Extreme Loads on a Wave Energy Conversion System," Energies, MDPI, vol. 15(15), pages 1-24, July.
    4. Hengxu Liu & Feng Yan & Fengmei Jing & Jingtao Ao & Zhaoliang Han & Fankai Kong, 2020. "Numerical and Experimental Investigation on a Moonpool-Buoy Wave Energy Converter," Energies, MDPI, vol. 13(9), pages 1-16, May.
    5. Alireza Shadmani & Mohammad Reza Nikoo & Riyadh I. Al-Raoush & Nasrin Alamdari & Amir H. Gandomi, 2022. "The Optimal Configuration of Wave Energy Conversions Respective to the Nearshore Wave Energy Potential," Energies, MDPI, vol. 15(20), pages 1-29, October.
    6. Stavropoulou, Charitini & Goude, Anders & Katsidoniotaki, Eirini & Göteman, Malin, 2023. "Fast time-domain model for the preliminary design of a wave power farm," Renewable Energy, Elsevier, vol. 219(P2).
    7. 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).
    8. Hong Li & Bo Zhang & Li Qiu & Shiyu Chen & Jianping Yuan & Jianjun Luo, 2019. "Advection-Based Coordinated Control for Wave-Energy Converter Array," Energies, MDPI, vol. 12(18), pages 1-21, September.
    9. Shadman, Milad & Guarniz Avalos, Gustavo Omar & Estefen, Segen F., 2021. "On the power performance of a wave energy converter with a direct mechanical drive power take-off system controlled by latching," Renewable Energy, Elsevier, vol. 169(C), pages 157-177.

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