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

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.

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Publication Details

Journal
Biomimetics
Published
2024-09-05
DOI
https://doi.org/10.3390/biomimetics9090536
Citations
2
Primary Topic
Optimization and Packing Problems
Type
article
Field-Weighted Citation Impact
0.70

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article

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

Gewen Huang, Haoyu Luo, Rongsen Jin, Yuanhang Qi et al.
2 citations
Biomimetics
Optimization and Packing Problems
0.70
article

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

Gewen Huang, Haoyu Luo, Rongsen Jin, Yuanhang Qi, Peng Hou, Yuhui Luo
article en
2 citations

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.

BiomimeticsVol. 9(9)
Guangdong University of Technology (CN), University of Electronic Science and Technology of China (CN), Jiaying University (CN), Chinese University of Hong Kong, Shenzhen (CN), Baidu (China) (CN)
Jiaying University, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 26%
Optimization and Packing Problems
0.70
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