Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/124311
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Type: Conference paper
Title: A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters
Author: Neshat, M.
Alexander, B.
Sergiienko, N.
Wagner, M.
Citation: GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, 2019 / vol.abs/1904.07043, pp.1293-1301
Publisher: ACM
Publisher Place: New York
Issue Date: 2019
ISBN: 9781450361118
Conference Name: GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic)
Statement of
Responsibility: 
Mehdi Neshat, Bradley Alexander, Nataliia Y. Sergiienko, and Markus Wagner
Abstract: Ocean wave energy is a source of renewable energy that has gained much attention for its potential to contribute significantly to meeting the global energy demand. In this research, we investigate the problem of maximising the energy delivered by farms of wave energy converters (WEC's). We consider state-of-the-art fully submerged three-tether converters deployed in arrays. The goal of this work is to use heuristic search to optimise the power output of arrays in a size-constrained environment by configuring WEC locations and the power-take-off (PTO) settings for each WEC. Modelling the complex hydrodynamic interactions in wave farms is expensive, which constrains search to only a few thousand model evaluations. We explore a variety of heuristic approaches including cooperative and hybrid methods. The effectiveness of these approaches is assessed in two real wave scenarios (Sydney and Perth) with farms of two different scales. We find that a combination of symmetric local search with Nelder-Mead Simplex direct search combined with a back-tracking optimization strategy is able to outperform previously defined search techniques by up to 3\%.
Rights: © 2019 Association for Computing Machinery.
RMID: 0030133049
DOI: 10.1145/3321707.3321806
Grant ID: http://purl.org/au-research/grants/arc/DE160100850
Published version: https://dl.acm.org/citation.cfm?id=3321707.3321806
Appears in Collections:Computer Science publications

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