Optimal array layout design of wave energy converter via honey badger algorithm

Oceans cover approximately 71 % of the Earth's surface, wave energy presents a promising avenue for development. Hence, wave energy has garnered significant attention. Wave energy converters (WECs) are crucial technologies that transform wave energy into electrical power. The power output of wa...

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Published inRenewable energy Vol. 234; p. 121182
Main Authors Yang, Bo, Zhou, Yimin, Liu, Bingqiang, Li, Miwei, Duan, Jinhang, Cao, Pulin, Zheng, Chao, Jiang, Lin, Sang, Yiyan
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.11.2024
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ISSN0960-1481
DOI10.1016/j.renene.2024.121182

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Summary:Oceans cover approximately 71 % of the Earth's surface, wave energy presents a promising avenue for development. Hence, wave energy has garnered significant attention. Wave energy converters (WECs) are crucial technologies that transform wave energy into electrical power. The power output of wave farms largely depends on the complex hydrodynamic interactions among WECs, which are influenced by buoy positions and wave environment. Therefore, optimizing the buoy positions is a key strategy for enhancing the overall power output of WEC arrays. This paper adopts a novel meta-heuristic algorithm (MhA) named honey badger algorithm (HBA), designed to fully explore and exploit the constructive interactions among three-tether fully submerged WECs. To validate the effectiveness of HBA in optimizing WEC layouts, four different wave farm configurations with 2, 4, 10, and 20 buoys are analyzed. A comprehensive comparison is conducted using five typical MhAs against HBA. Experimental results demonstrate that HBA achieves the highest total power output with remarkable stability compared to alternative methods. Specifically, the q-factor values for the four wave farms are improved to 1.039, 1.027, 1.056, and 0.969, respectively. Additionally, the total power output of the 10-buoy and 20-buoy wave farms can be increased by up to 7.19 % and 4.4 %, respectively.
ISSN:0960-1481
DOI:10.1016/j.renene.2024.121182