IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v188y2023ics1364032123007359.html
   My bibliography  Save this article

Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment

Author

Listed:
  • Pasta, Edoardo
  • Faedo, Nicolás
  • Mattiazzo, Giuliana
  • Ringwood, John V.

Abstract

Currently, a significant effort in the world research panorama is focused on finding efficient solutions to a carbon-free energy supply, wave energy being one of the most promising sources of untapped renewable energy. However, wave energy is not currently economic, though control technology has been shown to significantly increase the energy capture capabilities. Usually, the synthesis of a wave energy control strategy requires the adoption of control-oriented models, which are prone to error, particularly arising from unmodelled hydrodynamics, given the complexity of the hydrodynamic interactions between the device and the ocean. In this context, data-driven and data-based control strategies provide a potential solution to some of these issues, using real-time data to gather information about the system dynamics and performance. Thus motivated, this study provides a detailed analysis of different approaches to the exploitation of data in the design of control philosophies for wave energy systems, establishing clear definitions of data-driven and data-based control in this field, together with a classification highlighting the various roles of data in the control synthesis process. In particular, we investigate intrinsic opportunities and limitations behind the use of data in the process of control synthesis, providing a comprehensive review together with critical considerations aimed at directly contributing towards the development of efficient data-driven and data-based control systems for wave energy devices.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:188:y:2023:i:c:s1364032123007359
    DOI: 10.1016/j.rser.2023.113877
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123007359
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.113877?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mohammed Jama & Addy Wahyudie & Ali Assi & Hassan Noura, 2014. "An Intelligent Fuzzy Logic Controller for Maximum Power Capture of Point Absorbers," Energies, MDPI, vol. 7(6), pages 1-21, June.
    2. Aqiang Zhao & Weimin Wu & Zuoyao Sun & Lixun Zhu & Kaiyuan Lu & Henry Chung & Frede Blaabjerg, 2019. "A Flower Pollination Method Based Global Maximum Power Point Tracking Strategy for Point-Absorbing Type Wave Energy Converters," Energies, MDPI, vol. 12(7), pages 1-19, April.
    3. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    4. Lindroth, Simon & Leijon, Mats, 2011. "Offshore wave power measurements—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4274-4285.
    5. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.
    6. Penalba, Markel & Giorgi, Giussepe & Ringwood, John V., 2017. "Mathematical modelling of wave energy converters: A review of nonlinear approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1188-1207.
    7. Cuadra, L. & Salcedo-Sanz, S. & Nieto-Borge, J.C. & Alexandre, E. & Rodríguez, G., 2016. "Computational intelligence in wave energy: Comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1223-1246.
    8. 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.
    9. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    10. Windt, Christian & Davidson, Josh & Ringwood, John V., 2018. "High-fidelity numerical modelling of ocean wave energy systems: A review of computational fluid dynamics-based numerical wave tanks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 610-630.
    11. Reza Reisi, Ali & Hassan Moradi, Mohammad & Jamasb, Shahriar, 2013. "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 433-443.
    12. Rui Mendes & Maria Do Rosário Calado & Sílvio Mariano, 2018. "Maximum Power Point Tracking for a Point Absorber Device with a Tubular Linear Switched Reluctance Generator," Energies, MDPI, vol. 11(9), pages 1-18, August.
    13. Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
    14. Abdullah, M.A. & Yatim, A.H.M. & Tan, C.W. & Saidur, R., 2012. "A review of maximum power point tracking algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3220-3227.
    15. Clément, Alain & McCullen, Pat & Falcão, António & Fiorentino, Antonio & Gardner, Fred & Hammarlund, Karin & Lemonis, George & Lewis, Tony & Nielsen, Kim & Petroncini, Simona & Pontes, M. -Teresa & Sc, 2002. "Wave energy in Europe: current status and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 6(5), pages 405-431, October.
    16. 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).
    17. Ciri, Umberto & Rotea, Mario A. & Leonardi, Stefano, 2017. "Model-free control of wind farms: A comparative study between individual and coordinated extremum seeking," Renewable Energy, Elsevier, vol. 113(C), pages 1033-1045.
    18. Li, L. & Gao, Y. & Ning, D.Z. & Yuan, Z.M., 2021. "Development of a constraint non-causal wave energy control algorithm based on artificial intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    19. Farajvand, Mahdiyeh & Grazioso, Valerio & García-Violini, Demián & Ringwood, John V., 2023. "Uncertainty estimation in wave energy systems with applications in robust energy maximising control," Renewable Energy, Elsevier, vol. 203(C), pages 194-204.
    20. Astariz, S. & Iglesias, G., 2015. "The economics of wave energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 397-408.
    21. López, Iraide & Andreu, Jon & Ceballos, Salvador & Martínez de Alegría, Iñigo & Kortabarria, Iñigo, 2013. "Review of wave energy technologies and the necessary power-equipment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 413-434.
    22. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    23. Li, Liang & Yuan, Zhiming & Gao, Yan, 2018. "Maximization of energy absorption for a wave energy converter using the deep machine learning," Energy, Elsevier, vol. 165(PA), pages 340-349.
    24. Chan Roh & Kyong-Hwan Kim, 2022. "Deep Learning Prediction for Rotational Speed of Turbine in Oscillating Water Column-Type Wave Energy Converter," Energies, MDPI, vol. 15(2), pages 1-22, January.
    25. Martins, J.C. & Goulart, M.M. & Gomes, M. das N. & Souza, J.A. & Rocha, L.A.O. & Isoldi, L.A. & dos Santos, E.D., 2018. "Geometric evaluation of the main operational principle of an overtopping wave energy converter by means of Constructal Design," Renewable Energy, Elsevier, vol. 118(C), pages 727-741.
    26. Yue, Xuhui & Geng, Dazhou & Chen, Qijuan & Zheng, Yang & Gao, Gongzheng & Xu, Lei, 2021. "2-D lookup table based MPPT: Another choice of improving the generating capacity of a wave power system," Renewable Energy, Elsevier, vol. 179(C), pages 625-640.
    27. Guo, Bingyong & Ringwood, John V., 2021. "Geometric optimisation of wave energy conversion devices: A survey," Applied Energy, Elsevier, vol. 297(C).
    28. Penalba, Markel & Aizpurua, Jose Ignacio & Martinez-Perurena, Ander & Iglesias, Gregorio, 2022. "A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    29. 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.
    30. Alberdi, Mikel & Amundarain, Modesto & Garrido, Aitor & Garrido, Izaskun, 2012. "Neural control for voltage dips ride-through of oscillating water column-based wave energy converter equipped with doubly-fed induction generator," Renewable Energy, Elsevier, vol. 48(C), pages 16-26.
    31. Li, Ning & García-Medina, Gabriel & Cheung, Kwok Fai & Yang, Zhaoqing, 2021. "Wave energy resources assessment for the multi-modal sea state of Hawaii," Renewable Energy, Elsevier, vol. 174(C), pages 1036-1055.
    32. Jama, M.A. & Noura, H. & Wahyudie, A. & Assi, A., 2015. "Enhancing the performance of heaving wave energy converters using model-free control approach," Renewable Energy, Elsevier, vol. 83(C), pages 931-941.
    33. Sheng, Wanan, 2019. "Wave energy conversion and hydrodynamics modelling technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 482-498.
    34. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    35. 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.
    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. Shahroozi, Zahra & Göteman, Malin & Engström, Jens, 2024. "Neural network survivability approach of a wave energy converter considering uncertainties in the prediction of future knowledge," Renewable Energy, Elsevier, vol. 228(C).

    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. Papini, Guglielmo & Faedo, Nicolás & Mattiazzo, Giuliana, 2024. "Fault diagnosis and fault-tolerant control in wave energy: A perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    2. Guo, Bingyong & Ringwood, John V., 2021. "Geometric optimisation of wave energy conversion devices: A survey," Applied Energy, Elsevier, vol. 297(C).
    3. He, Guanghua & Luan, Zhengxiao & Zhang, Wei & He, Runhua & Liu, Chaogang & Yang, Kaibo & Yang, Changhao & Jing, Penglin & Zhang, Zhigang, 2023. "Review on research approaches for multi-point absorber wave energy converters," Renewable Energy, Elsevier, vol. 218(C).
    4. Ozkop, Emre & Altas, Ismail H., 2017. "Control, power and electrical components in wave energy conversion systems: A review of the technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 106-115.
    5. Manawadu, N.H.D.S. & Nissanka, I.D. & Karunasena, H.C.P., 2024. "SPH-based numerical modelling and performance analysis of a heaving point absorber type wave energy converter with a novel buoy geometry," Renewable Energy, Elsevier, vol. 228(C).
    6. Burgaç, Alper & Yavuz, Hakan, 2019. "Fuzzy Logic based hybrid type control implementation of a heaving wave energy converter," Energy, Elsevier, vol. 170(C), pages 1202-1214.
    7. 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).
    8. Kushal A. Prasad & Aneesh A. Chand & Nallapaneni Manoj Kumar & Sumesh Narayan & Kabir A. Mamun, 2022. "A Critical Review of Power Take-Off Wave Energy Technology Leading to the Conceptual Design of a Novel Wave-Plus-Photon Energy Harvester for Island/Coastal Communities’ Energy Needs," Sustainability, MDPI, vol. 14(4), pages 1-55, February.
    9. Zhang, Yongxing & Huang, Zhicong & Zou, Bowei & Bian, Jing, 2023. "Conceptual design and analysis for a novel parallel configuration-type wave energy converter," Renewable Energy, Elsevier, vol. 208(C), pages 627-644.
    10. Wang, Mangkuan & Shang, Jianzhong & Luo, Zirong & Lu, Zhongyue & Yao, Ganzhou, 2023. "Theoretical and numerical studies on improving absorption power of multi-body wave energy convert device with nonlinear bistable structure," Energy, Elsevier, vol. 282(C).
    11. Shadmani, Alireza & Nikoo, Mohammad Reza & Gandomi, Amir H. & Chen, Mingjie & Nazari, Rouzbeh, 2024. "Advancements in optimizing wave energy converter geometry utilizing metaheuristic algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    12. Galván-Pozos, D.E. & Sergiienko, N.Y. & García-Nava, H. & Ocampo-Torres, F.J. & Osuna-Cañedo, J.P., 2024. "Numerical analysis of the energy capture performance of a six-leg wave energy converter under Mexican waters wave conditions," Renewable Energy, Elsevier, vol. 228(C).
    13. Nasrollahi, Sadaf & Kazemi, Aliyeh & Jahangir, Mohammad-Hossein & Aryaee, Sara, 2023. "Selecting suitable wave energy technology for sustainable development, an MCDM approach," Renewable Energy, Elsevier, vol. 202(C), pages 756-772.
    14. 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).
    15. Younesian, Davood & Alam, Mohammad-Reza, 2017. "Multi-stable mechanisms for high-efficiency and broadband ocean wave energy harvesting," Applied Energy, Elsevier, vol. 197(C), pages 292-302.
    16. Rusu, Liliana & Onea, Florin, 2017. "The performance of some state-of-the-art wave energy converters in locations with the worldwide highest wave power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1348-1362.
    17. Amirinia, Gholamreza & Kamranzad, Bahareh & Mafi, Somayeh, 2017. "Wind and wave energy potential in southern Caspian Sea using uncertainty analysis," Energy, Elsevier, vol. 120(C), pages 332-345.
    18. Zhang, Yang & Zhao, Xuanlie & Geng, Jing & Göteman, Malin & Tao, Longbin, 2022. "Wave power extraction and coastal protection by a periodic array of oscillating buoys embedded in a breakwater," Renewable Energy, Elsevier, vol. 190(C), pages 434-456.
    19. Zou, Shangyan & Abdelkhalik, Ossama, 2020. "Time-varying linear quadratic Gaussian optimal control for three-degree-of-freedom wave energy converters," Renewable Energy, Elsevier, vol. 149(C), pages 217-225.
    20. Dongsheng Qiao & Rizwan Haider & Jun Yan & Dezhi Ning & Binbin Li, 2020. "Review of Wave Energy Converter and Design of Mooring System," Sustainability, MDPI, vol. 12(19), pages 1-31, October.

    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:eee:rensus:v:188:y:2023:i:c:s1364032123007359. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.