IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v237y2024ipcs0960148124017579.html
   My bibliography  Save this article

Design and testing of a Hardware-in-the-Loop system for a grid integrated Ocean Current Turbine

Author

Listed:
  • Fung, Sasha
  • Tang, Yufei
  • Nichols, Carter
  • VanZwieten, James
  • Mokari, Hassan
  • Alsenas, Gabriel

Abstract

Renewable energy is an increasingly vital field that continues to evolve with growing demands for innovative electricity production methods. Among these, Ocean Current Turbines (OCTs) have emerged as a promising technology that targets the vast energy potential of ocean currents. Building on established Hardware-in-the-Loop (HIL) methodologies primarily used in wind turbine testing, this research adapts and extends these techniques to develop a HIL testbed for OCTs. This research involves the development of a robust simulation model, designed to replicate the dynamic ocean current conditions impacting an OCT. The simulation is built with Simulink and is executed within Opal-RT’s real-time simulation environment. The simulation model consists of an OCT rotor model outputting desired shaft torque values to a dynamometer which uses measured torque and speed feedback to integrate with a micro-grid setup. This model aims to operate in both Region 2 and Region 3 of power generation, as well as the transitional Region 2.5. A variable-speed controller is developed to maximize the power capture in Region 2 and is evaluated on the HIL testbed, a blade-pitch controller is employed to prevent over-generation in Region 3 and is simulated inside the real-time environment, and both of these controllers are evaluated simultaneously while operating in region 2.5 on the HIL testbed. This study demonstrates a simple yet effective approach to HIL simulation for marine energy systems. The findings reinforce the feasibility of HIL testing for OCTs and contribute valuable insights into the broader context of marine renewable energy technology development.

Suggested Citation

  • Fung, Sasha & Tang, Yufei & Nichols, Carter & VanZwieten, James & Mokari, Hassan & Alsenas, Gabriel, 2024. "Design and testing of a Hardware-in-the-Loop system for a grid integrated Ocean Current Turbine," Renewable Energy, Elsevier, vol. 237(PC).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pc:s0960148124017579
    DOI: 10.1016/j.renene.2024.121689
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124017579
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.121689?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. Fontanella, Alessandro & Facchinetti, Alan & Daka, Elio & Belloli, Marco, 2023. "Modeling the coupled aero-hydro-servo-dynamic response of 15 MW floating wind turbines with wind tunnel hardware in the loop," Renewable Energy, Elsevier, vol. 219(P1).
    2. Hawari, Qusay & Kim, Taeseong & Ward, Christopher & Fleming, James, 2022. "A robust gain scheduling method for a PI collective pitch controller of multi-MW onshore wind turbines," Renewable Energy, Elsevier, vol. 192(C), pages 443-455.
    3. Ouhrouche, Mohand, 2009. "Transient analysis of a grid connected wind driven induction generator using a real-time simulation platform," Renewable Energy, Elsevier, vol. 34(3), pages 801-806.
    4. Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Jiang, Di, 2020. "Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines," Renewable Energy, Elsevier, vol. 145(C), pages 981-996.
    5. Gan, Leong Kit & Echenique Subiabre, Estanislao Juan Pablo, 2019. "A realistic laboratory development of an isolated wind-battery system," Renewable Energy, Elsevier, vol. 136(C), pages 645-656.
    6. Boukhezzar, B. & Lupu, L. & Siguerdidjane, H. & Hand, M., 2007. "Multivariable control strategy for variable speed, variable pitch wind turbines," Renewable Energy, Elsevier, vol. 32(8), pages 1273-1287.
    7. Razi, P. & Ramaprabhu, P. & Tarey, P. & Muglia, M. & Vermillion, C., 2022. "A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions," Renewable Energy, Elsevier, vol. 200(C), pages 1602-1617.
    8. Yang, Bo & Yu, Tao & Shu, Hongchun & Zhang, Yuming & Chen, Jian & Sang, Yiyan & Jiang, Lin, 2018. "Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine," Renewable Energy, Elsevier, vol. 119(C), pages 577-589.
    9. Yang, Xiufeng & Haas, Kevin A. & Fritz, Hermann M., 2014. "Evaluating the potential for energy extraction from turbines in the gulf stream system," Renewable Energy, Elsevier, vol. 72(C), pages 12-21.
    10. Taesu Jeon & Dongmyoung Kim & Insu Paek, 2022. "Improvements to and Experimental Validation of PI Controllers Using a Reference Bias Control Algorithm for Wind Turbines," Energies, MDPI, vol. 15(21), pages 1-18, November.
    11. Ruuskanen, Vesa & Koponen, Joonas & Sillanpää, Teemu & Huoman, Kimmo & Kosonen, Antti & Niemelä, Markku & Ahola, Jero, 2018. "Design and implementation of a power-hardware-in-loop simulator for water electrolysis emulation," Renewable Energy, Elsevier, vol. 119(C), pages 106-115.
    Full references (including those not matched with items on IDEAS)

    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. Youcef Belkhier & Abdelyazid Achour & Rabindra Nath Shaw & Nasim Ullah & Md. Shahariar Chowdhury & Kuaanan Techato, 2021. "Energy-Based Combined Nonlinear Observer and Voltage Controller for a PMSG Using Fuzzy Supervisor High Order Sliding Mode in a Marine Current Power System," Sustainability, MDPI, vol. 13(7), pages 1-24, March.
    2. Mérida, Jován & Aguilar, Luis T. & Dávila, Jorge, 2014. "Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization," Renewable Energy, Elsevier, vol. 71(C), pages 715-728.
    3. Fan, Zhixin & Zhu, Caichao, 2019. "The optimization and the application for the wind turbine power-wind speed curve," Renewable Energy, Elsevier, vol. 140(C), pages 52-61.
    4. Yolanda Vidal & Leonardo Acho & Ningsu Luo & Mauricio Zapateiro & Francesc Pozo, 2012. "Power Control Design for Variable-Speed Wind Turbines," Energies, MDPI, vol. 5(8), pages 1-18, August.
    5. Taehyung Koo & Rockkil Ko & Dongwoo Ha & Jaeyoung Han, 2023. "Development of Model-Based PEM Water Electrolysis HILS (Hardware-in-the-Loop Simulation) System for State Evaluation and Fault Detection," Energies, MDPI, vol. 16(8), pages 1-18, April.
    6. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    7. Wang, Hao & Wang, Tongguang & Ke, Shitang & Hu, Liang & Xie, Jiaojie & Cai, Xin & Cao, Jiufa & Ren, Yuxin, 2023. "Assessing code-based design wind loads for offshore wind turbines in China against typhoons," Renewable Energy, Elsevier, vol. 212(C), pages 669-682.
    8. Nikita Tomin, 2023. "Robust Reinforcement Learning-Based Multiple Inputs and Multiple Outputs Controller for Wind Turbines," Mathematics, MDPI, vol. 11(14), pages 1-19, July.
    9. Soudan, Bassel, 2019. "Community-scale baseload generation from marine energy," Energy, Elsevier, vol. 189(C).
    10. Niancheng Zhou & Fan Ye & Qianggang Wang & Xiaoxuan Lou & Yuxiang Zhang, 2016. "Short-Circuit Calculation in Distribution Networks with Distributed Induction Generators," Energies, MDPI, vol. 9(4), pages 1-21, April.
    11. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    12. Arabgolarcheh, Alireza & Rouhollahi, Amirhossein & Benini, Ernesto, 2023. "Analysis of middle-to-far wake behind floating offshore wind turbines in the presence of multiple platform motions," Renewable Energy, Elsevier, vol. 208(C), pages 546-560.
    13. Huazhen Cao & Chong Gao & Xuan He & Yang Li & Tao Yu, 2020. "Multi-Agent Cooperation Based Reduced-Dimension Q(?) Learning for Optimal Carbon-Energy Combined-Flow," Energies, MDPI, vol. 13(18), pages 1-22, September.
    14. Takhi, Hocine & Kemih, Karim & Moysis, Lazaros & Volos, Christos, 2021. "Passivity based sliding mode control and synchronization of a perturbed uncertain unified chaotic system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 150-169.
    15. Sofiane Bououden & Fouad Allouani & Abdelaziz Abboudi & Mohammed Chadli & Ilyes Boulkaibet & Zaher Al Barakeh & Bilel Neji & Raymond Ghandour, 2023. "Observer-Based Robust Fault Predictive Control for Wind Turbine Time-Delay Systems with Sensor and Actuator Faults," Energies, MDPI, vol. 16(2), pages 1-21, January.
    16. Chen, Jiahao & Hu, Zhiqiang & Liu, Geliang & Wan, Decheng, 2019. "Coupled aero-hydro-servo-elastic methods for floating wind turbines," Renewable Energy, Elsevier, vol. 130(C), pages 139-153.
    17. Li, Tenghui & Yang, Jin & Ioannou, Anastasia, 2024. "Data-driven control of wind turbine under online power strategy via deep learning and reinforcement learning," Renewable Energy, Elsevier, vol. 234(C).
    18. Han, Chenlu & Nagamune, Ryozo, 2020. "Platform position control of floating wind turbines using aerodynamic force," Renewable Energy, Elsevier, vol. 151(C), pages 896-907.
    19. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
    20. Huang, Yu & Li, Sijun & Zhang, Peng & Wang, Dongfeng & Lan, Jianjiang & Lee, Kwang Y. & Zhang, Qiliang, 2024. "Parameter adaptive stochastic model predictive control for wind–solar–hydrogen coupled power system," Renewable Energy, Elsevier, vol. 237(PA).

    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:renene:v:237:y:2024:i:pc:s0960148124017579. 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/renewable-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.