A Comparative Study of Metaheuristic Algorithms for Wave Energy Converter Power Take-Off Optimisation: A Case Study for Eastern Australia

Journal of Marine Science and Engineering

Meysam Majidi Nezhad¹  Oğuzhan Ceylan²  Erfan Amini³  Rojin Asadi³  Danial Golbaz³  Mehdi Neshat⁴  Mahdieh Nasiri⁵ 

[1] Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Rome, Italy;Management Information Systems Department, Kadir Has University, Istanbul 34083, Turkey;School of Civil Engineering, College of Engineering, University of Tehran, Tehran 13145, Iran;School of Computer Science, The University of Adelaide, Adelaide 5005, Australia;School of Mechanical Engineering, Iran University of Science and Technology, Tehran 13114, Iran;

关键词: wave energy converter;    power take-off system;    optimisation;    evolutionary algorithms;    metaheuristic;   

DOI  :  10.3390/jmse9050490

来源: DOAJ

【 摘 要 】

One of the most encouraging sorts of renewable energy is ocean wave energy. In spite of a large number of investigations in this field during the last decade, wave energy technologies are recognised as neither mature nor broadly commercialised compared to other renewable energy technologies. In this paper, we develop and optimise Power Take-off (PTO) configurations of a well-known wave energy converter (WEC) called a point absorber. This WEC is a fully submerged buoy with three tethers, which was proposed and developed by Carnegie Clean Energy Company in Australia. Optimising the WEC’s PTO parameters is a challenging engineering problem due to the high dimensionality and complexity of the search space. This research compares the performance of five state-of-the-art metaheuristics (including Covariance Matrix Adaptation Evolution Strategy, Gray Wolf optimiser, Harris Hawks optimisation, and Grasshopper Optimisation Algorithm) based on the real wave scenario in Sydney sea state. The experimental achievements show that the Multiverse optimisation (MVO) algorithm performs better than the other metaheuristics applied in this work.

【 授权许可】

Unknown