Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters|
|Citation:||GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, 2019 / vol.abs/1904.07043, pp.1293-1301|
|Publisher Place:||New York|
|Conference Name:||GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic)|
|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.|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.