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

Mehdi Neshat, Nataliia Y. Sergiienko, Seyedali Mirjalili, Meysam Majidi Nezhad, Giuseppe Piras and Davide Astiaso Garcia
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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
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

Energies, 2021, vol. 14, issue 13, 1-17

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.

Keywords: renewable energy systems; wave energy converters; power take-off; bio-inspired; meta-heuristics; optimisation algorithms; Moth Flame Optimisation; evolutionary algorithms; swarm intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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