IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v165y2018ipap340-349.html
   My bibliography  Save this article

Maximization of energy absorption for a wave energy converter using the deep machine learning

Author

Listed:
  • Li, Liang
  • Yuan, Zhiming
  • Gao, Yan

Abstract

A controller is usually used to maximize the energy absorption of wave energy converter. Despite the development of various control strategies, the practical implementation of wave energy control is still difficult since the control inputs are the future wave forces. In this work, the artificial intelligence technique is adopted to tackle this problem. A multi-layer artificial neural network is developed and trained by the deep machine learning algorithm to forecast the short-term wave forces. The model predictive control strategy is used to implement real-time latching control action to a heaving point-absorber. Simulation results show that the average energy absorption is increased substantially with the controller. Since the future wave forces are predicted, the controller is applicable to a full-scale wave energy converter in practice. Further analysis indicates that the prediction error has a negative effect on the control performance, leading to the reduction of energy absorption.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:165:y:2018:i:pa:p:340-349
    DOI: 10.1016/j.energy.2018.09.093
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218318577
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2018.09.093?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. Li, Guang & Weiss, George & Mueller, Markus & Townley, Stuart & Belmont, Mike R., 2012. "Wave energy converter control by wave prediction and dynamic programming," Renewable Energy, Elsevier, vol. 48(C), pages 392-403.
    2. Li, Liang & Gao, Yan & Yuan, Zhiming & Day, Sandy & Hu, Zhiqiang, 2018. "Dynamic response and power production of a floating integrated wind, wave and tidal energy system," Renewable Energy, Elsevier, vol. 116(PA), pages 412-422.
    3. Falcão, António F.O. & Henriques, João C.C., 2016. "Oscillating-water-column wave energy converters and air turbines: A review," Renewable Energy, Elsevier, vol. 85(C), pages 1391-1424.
    4. Muliawan, Made Jaya & Karimirad, Madjid & Moan, Torgeir, 2013. "Dynamic response and power performance of a combined Spar-type floating wind turbine and coaxial floating wave energy converter," Renewable Energy, Elsevier, vol. 50(C), pages 47-57.
    5. Li, Liang & Gao, Yan & Hu, Zhiqiang & Yuan, Zhiming & Day, Sandy & Li, Haoran, 2018. "Model test research of a semisubmersible floating wind turbine with an improved deficient thrust force correction approach," Renewable Energy, Elsevier, vol. 119(C), pages 95-105.
    6. Henriques, J.C.C. & Gato, L.M.C. & Falcão, A.F.O. & Robles, E. & Faÿ, F.-X., 2016. "Latching control of a floating oscillating-water-column wave energy converter," Renewable Energy, Elsevier, vol. 90(C), pages 229-241.
    7. He, Fang & Huang, Zhenhua & Law, Adrian Wing-Keung, 2013. "An experimental study of a floating breakwater with asymmetric pneumatic chambers for wave energy extraction," Applied Energy, Elsevier, vol. 106(C), pages 222-231.
    8. Xiao, Xiaolong & Xiao, Longfei & Peng, Tao, 2017. "Comparative study on power capture performance of oscillating-body wave energy converters with three novel power take-off systems," Renewable Energy, Elsevier, vol. 103(C), pages 94-105.
    9. Bahaj, A.S. & Batten, W.M.J. & McCann, G., 2007. "Experimental verifications of numerical predictions for the hydrodynamic performance of horizontal axis marine current turbines," Renewable Energy, Elsevier, vol. 32(15), pages 2479-2490.
    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. Neshat, Mehdi & Nezhad, Meysam Majidi & Sergiienko, Nataliia Y. & Mirjalili, Seyedali & Piras, Giuseppe & Garcia, Davide Astiaso, 2022. "Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser," Energy, Elsevier, vol. 256(C).
    2. Li, Yunzhu & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Deep learning based real-time energy extraction system modeling for flapping foil," Energy, Elsevier, vol. 246(C).
    3. 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.
    4. 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.
    5. Samuel-Soma M. Ajibade & Festus Victor Bekun & Festus Fatai Adedoyin & Bright Akwasi Gyamfi & Anthonia Oluwatosin Adediran, 2023. "Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)," Clean Technol., MDPI, vol. 5(2), pages 1-21, April.
    6. Liang, Hongjian & Qin, Hao & Su, Haowen & Wen, Zhixuan & Mu, Lin, 2024. "Environmental-Sensing and adaptive optimization of wave energy converter based on deep reinforcement learning and computational fluid dynamics," Energy, Elsevier, vol. 297(C).
    7. 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).
    8. Abbas, Ahmed K. & Bashikh, Ali A. & Abbas, Hayder & Mohammed, Haider Q., 2019. "Intelligent decisions to stop or mitigate lost circulation based on machine learning," Energy, Elsevier, vol. 183(C), pages 1104-1113.
    9. Aleix Maria-Arenas & Aitor J. Garrido & Eugen Rusu & Izaskun Garrido, 2019. "Control Strategies Applied to Wave Energy Converters: State of the Art," Energies, MDPI, vol. 12(16), pages 1-19, August.
    10. Zhao, Huai & Zhang, Haicheng & Bi, Rengui & Xi, Ru & Xu, Daolin & Shi, Qijia & Wu, Bo, 2020. "Enhancing efficiency of a point absorber bistable wave energy converter under low wave excitations," Energy, Elsevier, vol. 212(C).
    11. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
    12. Lu, Kai-Hung & Hong, Chih-Ming & Xu, Qiangqiang, 2019. "Recurrent wavelet-based Elman neural network with modified gravitational search algorithm control for integrated offshore wind and wave power generation systems," Energy, Elsevier, vol. 170(C), pages 40-52.
    13. 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).
    14. Gao, Ruobin & Li, Ruilin & Hu, Minghui & Suganthan, Ponnuthurai Nagaratnam & Yuen, Kum Fai, 2023. "Dynamic ensemble deep echo state network for significant wave height forecasting," Applied Energy, Elsevier, vol. 329(C).
    15. 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).
    16. Liu, Yue & Zhang, Xiantao & Dong, Qing & Chen, Gang & Li, Xin, 2024. "Phase-resolved wave prediction with linear wave theory and physics-informed neural networks," Applied Energy, Elsevier, vol. 355(C).
    17. Del Pozo Gonzalez, Hector & Bianchi, Fernando D. & Dominguez-Garcia, Jose Luis & Gomis-Bellmunt, Oriol, 2023. "Co-located wind-wave farms: Optimal control and grid integration," Energy, Elsevier, vol. 272(C).
    18. Mahmoodi, Kumars & Nepomuceno, Erivelton & Razminia, Abolhassan, 2022. "Wave excitation force forecasting using neural networks," Energy, Elsevier, vol. 247(C).
    19. Han, Zhi & Cao, Feifei & Tao, Ji & Shi, Hongda, 2023. "Study on the energy capture spectrum (ECS) of a multi-DoF buoy under random waves," Energy, Elsevier, vol. 279(C).
    20. 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).

    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. Li, Liang & Cheng, Zhengshun & Yuan, Zhiming & Gao, Yan, 2018. "Short-term extreme response and fatigue damage of an integrated offshore renewable energy system," Renewable Energy, Elsevier, vol. 126(C), pages 617-629.
    2. Li, Liang & Yuan, Zhi-Ming & Gao, Yan & Zhang, Xinshu & Tezdogan, Tahsin, 2019. "Investigation on long-term extreme response of an integrated offshore renewable energy device with a modified environmental contour method," Renewable Energy, Elsevier, vol. 132(C), pages 33-42.
    3. Li, Ming & Luo, Haojie & Zhou, Shijie & Senthil Kumar, Gokula Manikandan & Guo, Xinman & Law, Tin Chung & Cao, Sunliang, 2022. "State-of-the-art review of the flexibility and feasibility of emerging offshore and coastal ocean energy technologies in East and Southeast Asia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    4. Yu Zhou & Chongwei Zhang & Dezhi Ning, 2018. "Hydrodynamic Investigation of a Concentric Cylindrical OWC Wave Energy Converter," Energies, MDPI, vol. 11(4), pages 1-23, April.
    5. Li, Liang & Gao, Yan & Yuan, Zhiming & Day, Sandy & Hu, Zhiqiang, 2018. "Dynamic response and power production of a floating integrated wind, wave and tidal energy system," Renewable Energy, Elsevier, vol. 116(PA), pages 412-422.
    6. Correia da Fonseca, F.X. & Henriques, J.C.C. & Gato, L.M.C. & Falcão, A.F.O., 2019. "Oscillating flow rig for air turbine testing," Renewable Energy, Elsevier, vol. 142(C), pages 373-382.
    7. Nicola Delmonte & Eider Robles & Paolo Cova & Francesco Giuliani & François Xavier Faÿ & Joseba Lopez & Piero Ruol & Luca Martinelli, 2020. "An Iterative Refining Approach to Design the Control of Wave Energy Converters with Numerical Modeling and Scaled HIL Testing," Energies, MDPI, vol. 13(10), pages 1-19, May.
    8. Sun, Pengyuan & Liu, Senming & He, Hongzhou & Zhao, Yingru & Zheng, Songgen & Chen, Hu & Yang, Shaohui, 2021. "Simulated and experimental investigation of a floating-array-buoys wave energy converter with single-point mooring," Renewable Energy, Elsevier, vol. 176(C), pages 637-650.
    9. Portillo, J.C.C. & Collins, K.M. & Gomes, R.P.F. & Henriques, J.C.C. & Gato, L.M.C. & Howey, B.D. & Hann, M.R. & Greaves, D.M. & Falcão, A.F.O., 2020. "Wave energy converter physical model design and testing: The case of floating oscillating-water-columns," Applied Energy, Elsevier, vol. 278(C).
    10. Zheng, Siming & Zhu, Guixun & Simmonds, David & Greaves, Deborah & Iglesias, Gregorio, 2020. "Wave power extraction from a tubular structure integrated oscillating water column," Renewable Energy, Elsevier, vol. 150(C), pages 342-355.
    11. Mohd Afifi Jusoh & Mohd Zamri Ibrahim & Muhamad Zalani Daud & Aliashim Albani & Zulkifli Mohd Yusop, 2019. "Hydraulic Power Take-Off Concepts for Wave Energy Conversion System: A Review," Energies, MDPI, vol. 12(23), pages 1-23, November.
    12. Ji, Xueyu & Shami, Elie Al & Monty, Jason & Wang, Xu, 2020. "Modelling of linear and non-linear two-body wave energy converters under regular and irregular wave conditions," Renewable Energy, Elsevier, vol. 147(P1), pages 487-501.
    13. Guo, Baoming & Ning, Dezhi & Wang, Rongquan & Ding, Boyin, 2021. "Hydrodynamics of an oscillating water column WEC - Breakwater integrated system with a pitching front-wall," Renewable Energy, Elsevier, vol. 176(C), pages 67-80.
    14. Zhao, Xuanlie & Zhang, Lidong & Li, Mingwei & Johanning, Lars, 2021. "Experimental investigation on the hydrodynamic performance of a multi-chamber OWC-breakwater," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    15. Correia da Fonseca, F.X. & Gomes, R.P.F. & Henriques, J.C.C. & Gato, L.M.C. & Falcão, A.F.O., 2016. "Model testing of an oscillating water column spar-buoy wave energy converter isolated and in array: Motions and mooring forces," Energy, Elsevier, vol. 112(C), pages 1207-1218.
    16. Henriques, J.C.C. & Portillo, J.C.C. & Gato, L.M.C. & Gomes, R.P.F. & Ferreira, D.N. & Falcão, A.F.O., 2016. "Design of oscillating-water-column wave energy converters with an application to self-powered sensor buoys," Energy, Elsevier, vol. 112(C), pages 852-867.
    17. Kamarlouei, M. & Gaspar, J.F. & Calvario, M. & Hallak, T.S. & Mendes, M.J.G.C. & Thiebaut, F. & Guedes Soares, C., 2022. "Experimental study of wave energy converter arrays adapted to a semi-submersible wind platform," Renewable Energy, Elsevier, vol. 188(C), pages 145-163.
    18. Li, Liang, 2022. "Full-coupled analysis of offshore floating wind turbine supported by very large floating structure with consideration of hydroelasticity," Renewable Energy, Elsevier, vol. 189(C), pages 790-799.
    19. da Silva, L.S.P. & Sergiienko, N.Y. & Cazzolato, B. & Ding, B., 2022. "Dynamics of hybrid offshore renewable energy platforms: Heaving point absorbers connected to a semi-submersible floating offshore wind turbine," Renewable Energy, Elsevier, vol. 199(C), pages 1424-1439.
    20. Wang, Chen & Zhang, Yongliang & Deng, Zhengzhi, 2022. "Wave power extraction for an oscillating water column device consisting of a surging front and back lip-wall: An analytical study," Renewable Energy, Elsevier, vol. 184(C), pages 100-114.

    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:energy:v:165:y:2018:i:pa:p:340-349. 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.journals.elsevier.com/energy .

    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.