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An Intelligent Fuzzy Logic Controller for Maximum Power Capture of Point Absorbers

Author

Listed:
  • Mohammed Jama

    (Electrical Engineering Department, United Arab Emirates University, Al Ain, P.O. Box 15551, UAE)

  • Addy Wahyudie

    (Electrical Engineering Department, United Arab Emirates University, Al Ain, P.O. Box 15551, UAE)

  • Ali Assi

    (Department of Electrical and Electronics Engineering, Lebanese International University, Beirut, P.O. Box 146404, Lebanon)

  • Hassan Noura

    (Electrical Engineering Department, United Arab Emirates University, Al Ain, P.O. Box 15551, UAE)

Abstract

This article presents an intelligent fuzzy logic controller (FLC) for controlling single-body heaving wave energy converter (WEC) or what is widely known as “Point Absorber”. The controller aims at maximizing the energy captured from the sea waves. The power take-off (PTO) limitations are addressed implicitly in the fuzzy inference system (FIS) framework. In order to enhance the WEC power capturing bandwidth and make it less susceptible to wave environment irregularities and the system parametric uncertainties, the controller is built to have a self-configurable capability. This also eliminates the need to repeatedly run in-situ tuning procedure of the fuzzy controller or switch between several controllers based on the operating conditions. The fuzzy membership functions (MFs) are optimally tuned using particle swarm optimization (PSO) algorithm. To alleviate the computational burden associated with performing on-line optimization, the fuzzy controller is tuned at a rate significantly lower than the system sampling time. The suggested PSO-FLC has shown promising results compared with the fixed structure fuzzy logic controller (FS-FLC) and other passive control strategies. Several computer simulations were carried out to evaluate the controller effectiveness by applying different sea-states and analyzing the resultant WEC dynamics.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:6:p:4033-4053:d:37430
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    References listed on IDEAS

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    1. Azadegan, Arash & Porobic, Lejla & Ghazinoory, Sepehr & Samouei, Parvaneh & Saman Kheirkhah, Amir, 2011. "Fuzzy logic in manufacturing: A review of literature and a specialized application," International Journal of Production Economics, Elsevier, vol. 132(2), pages 258-270, August.
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    Cited by:

    1. Wahyudie, A. & Jama, M.A. & Saeed, O. & Noura, H. & Assi, A. & Harib, K., 2015. "Robust and low computational cost controller for improving captured power in heaving wave energy converters," Renewable Energy, Elsevier, vol. 82(C), pages 114-124.
    2. Josh Davidson & John V. Ringwood, 2017. "Mathematical Modelling of Mooring Systems for Wave Energy Converters—A Review," Energies, MDPI, vol. 10(5), pages 1-46, May.
    3. Marcin Drzewiecki & Jarosław Guziński, 2020. "Fuzzy Control of Waves Generation in a Towing Tank," Energies, MDPI, vol. 13(8), pages 1-17, April.
    4. 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.
    5. 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.
    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. 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.
    8. Pablo Zambrana & Javier Fernandez-Quijano & J. Jesus Fernandez-Lozano & Pedro M. Mayorga Rubio & Alfonso J. Garcia-Cerezo, 2021. "Improving the Performance of Controllers for Wind Turbines on Semi-Submersible Offshore Platforms: Fuzzy Supervisor Control," Energies, MDPI, vol. 14(19), pages 1-17, September.

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