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Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm

Author

Listed:
  • Mehdi Neshat

    (Center for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD 4006, Australia)

  • Nataliia Y. Sergiienko

    (School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5001, Australia)

  • Seyedali Mirjalili

    (Center for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD 4006, Australia
    Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea)

  • Meysam Majidi Nezhad

    (Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00197 Rome, Italy)

  • Giuseppe Piras

    (Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00197 Rome, Italy)

  • Davide Astiaso Garcia

    (Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, 00197 Rome, Italy)

Abstract

Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters’ optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth–Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC.

Suggested Citation

  • Mehdi Neshat & Nataliia Y. Sergiienko & Seyedali Mirjalili & Meysam Majidi Nezhad & Giuseppe Piras & Davide Astiaso Garcia, 2021. "Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm," Energies, MDPI, vol. 14(13), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3737-:d:579845
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    References listed on IDEAS

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    1. 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.
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    3. Mehdi Neshat & Nataliia Y. Sergiienko & Erfan Amini & Meysam Majidi Nezhad & Davide Astiaso Garcia & Bradley Alexander & Markus Wagner, 2020. "A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea," Energies, MDPI, vol. 13(20), pages 1-23, October.
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    Cited by:

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    2. Tiesheng Liu & Yanjun Liu & Shuting Huang & Gang Xue, 2022. "Shape Optimization of Oscillating Buoy Wave Energy Converter Based on the Mean Annual Power Prediction Model," Energies, MDPI, vol. 15(20), pages 1-19, October.
    3. Foteinis, Spyros, 2022. "Wave energy converters in low energy seas: Current state and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

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