Optimization of power take-off system settings and regional site selection procedure for a wave energy converter

Mehdipour, H; Amini, E; Naeeni, ST; Neshat, M; Gandomi, AH

Optimization of power take-off system settings and regional site selection procedure for a wave energy converter

Mehdipour, H Amini, E Naeeni, ST Neshat, M

Gandomi, AH

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Energy Conversion and Management: X, 2024, 22, pp. 100559
Issue Date:: 2024-04-01

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Field	Value	Language
dc.contributor.author	Mehdipour, H
dc.contributor.author	Amini, E
dc.contributor.author	Naeeni, ST
dc.contributor.author	Neshat, M https://orcid.org/0000-0002-9537-9513
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-03-28T11:20:14Z
dc.date.available	2024-03-28T11:20:14Z
dc.date.issued	2024-04-01
dc.identifier.citation	Energy Conversion and Management: X, 2024, 22, pp. 100559
dc.identifier.issn	2590-1745
dc.identifier.issn	2590-1745
dc.identifier.uri	http://hdl.handle.net/10453/177355
dc.description.abstract	Ocean wave energy stands as a crucial component in the quest for sustainable and renewable energy sources, essential in the global effort to mitigate climate change. However, a significant challenge in this field is optimizing the efficiency of Wave Energy Converters (WECs) on a regional scale, particularly Oscillating Surge Wave Energy Converters (OSWECs). This challenge stems from the complex, nonlinear interactions between ocean waves and these devices, necessitating precise tuning of Power Take-Off (PTO) system settings and optimal placement for the highest possible performance and stability. To address this challenge, our study introduces the Hill Climb - Explorative Grey Wolf Optimizer (HC-EGWO), a novel algorithm combining local search and swarm-based global optimization strategies. This hybrid approach effectively balances exploration and exploitation in the solution space, leading to more optimal PTO settings for OSWECs. Alongside this algorithmic development, we conduct a thorough feasibility analysis based on the constraints of the flap's maximum angular motion. This ensures the optimized OSWEC operates within safe and efficient limits. In a comparative analysis with the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), the artificial Gorilla Troops Optimizer (GTO), and different implementations of the GWO, our results show an improvement in power output, with the HC-EGWO method achieving up to a 3.31% increase over other variations of the GWO and 45% increase compared to all the methods. The findings of this study not only demonstrate the effectiveness of the HC-EGWO method but also provide strategic insights for the deployment of OSWECs in areas like the South Caspian Sea, where unique environmental factors imply careful consideration in the site selection process.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Energy Conversion and Management: X
dc.relation.isbasedon	10.1016/j.ecmx.2024.100559
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Optimization of power take-off system settings and regional site selection procedure for a wave energy converter
dc.type	Journal Article
utslib.citation.volume	22
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-28T11:20:05Z
pubs.publication-status	Accepted
pubs.volume	22

Abstract:

Ocean wave energy stands as a crucial component in the quest for sustainable and renewable energy sources, essential in the global effort to mitigate climate change. However, a significant challenge in this field is optimizing the efficiency of Wave Energy Converters (WECs) on a regional scale, particularly Oscillating Surge Wave Energy Converters (OSWECs). This challenge stems from the complex, nonlinear interactions between ocean waves and these devices, necessitating precise tuning of Power Take-Off (PTO) system settings and optimal placement for the highest possible performance and stability. To address this challenge, our study introduces the Hill Climb - Explorative Grey Wolf Optimizer (HC-EGWO), a novel algorithm combining local search and swarm-based global optimization strategies. This hybrid approach effectively balances exploration and exploitation in the solution space, leading to more optimal PTO settings for OSWECs. Alongside this algorithmic development, we conduct a thorough feasibility analysis based on the constraints of the flap's maximum angular motion. This ensures the optimized OSWEC operates within safe and efficient limits. In a comparative analysis with the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), the artificial Gorilla Troops Optimizer (GTO), and different implementations of the GWO, our results show an improvement in power output, with the HC-EGWO method achieving up to a 3.31% increase over other variations of the GWO and 45% increase compared to all the methods. The findings of this study not only demonstrate the effectiveness of the HC-EGWO method but also provide strategic insights for the deployment of OSWECs in areas like the South Caspian Sea, where unique environmental factors imply careful consideration in the site selection process.

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