Mother–Daughter Vessel Operation and Maintenance Routing Optimization for Offshore Wind Farms Using Restructuring Particle Swarm Optimization

Author:

Qi Yuanhang1,Luo Haoyu12,Huang Gewen3ORCID,Hou Peng4,Jin Rongsen5,Luo Yuhui2

Affiliation:

1. School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China

2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China

3. Information and Network Center, Jiaying University, Meizhou 514015, China

4. Smart Wind Energy Team, Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310052, China

5. The Chinese University of Hong Kong (Shenzhen), Shenzhen 518116, China

Abstract

As the capacity of individual offshore wind turbines increases, prolonged downtime (due to maintenance or faults) will result in significant economic losses. This necessitates enhancing the efficiency of vessel operation and maintenance (O&M) to reduce O&M costs. Existing research mostly focuses on planning O&M schemes for individual vessels. However, there exists a research gap in the scientific scheduling for state-of-the-art O&M vessels. To bridge this gap, this paper considers the use of an advanced O&M vessel in the O&M process, taking into account the downtime costs associated with wind turbine maintenance and repair incidents. A mathematical model is constructed with the objective of minimizing overall O&M expenditure. Building upon this formulation, this paper introduces a novel restructuring particle swarm optimization which is tailed with a bespoke encoding and decoding strategy, designed to yield an optimized solution that aligns with the intricate demands of the problem at hand. The simulation results indicate that the proposed method can achieve significant savings of 28.85% in O&M costs. The outcomes demonstrate the algorithm’s proficiency in tackling the model efficiently and effectively.

Funder

Guangdong Basic and Applied Basic Research Foundation

the Key Project in Higher Education of Guangdong Province, China

the Social Public Welfare and Basic Research Project of Zhongshan City

the research project of Jiaying University

Publisher

MDPI AG

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3