Maintenance Strategy for Offshore Wind Farms Based on Optimal Maintenance Time Windows

This paper presents a maintenance strategy for offshore wind farms that focuses on optimal time windows for maintenance activities. Firstly, a health assessment model and operation regression model for units are constructed based on SCADA data to determine the operating status of the units. Then, considering constraints such as grid maintenance plans, offshore weather conditions, and unit characteristics, an optimization model is established with the objective of minimizing wind power losses and maintenance costs to find the optimal maintenance time window. Lastly, a case study is conducted, and the results indicate that the proposed maintenance strategy can increase wind power annual operating hours by more than 1%.

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Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/appeec61255.2024.10922531
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

Maintenance Strategy for Offshore Wind Farms Based on Optimal Maintenance Time Windows

Honghai Chen, Yunpeng Cheng, Fan Wu, Bin Sun et al.
Machine Fault Diagnosis Techniques
article

Maintenance Strategy for Offshore Wind Farms Based on Optimal Maintenance Time Windows

Honghai Chen, Yunpeng Cheng, Fan Wu, Bin Sun, Xiao Ming Yang, Jingjing Bai
article en

Abstract

This paper presents a maintenance strategy for offshore wind farms that focuses on optimal time windows for maintenance activities. Firstly, a health assessment model and operation regression model for units are constructed based on SCADA data to determine the operating status of the units. Then, considering constraints such as grid maintenance plans, offshore weather conditions, and unit characteristics, an optimization model is established with the objective of minimizing wind power losses and maintenance costs to find the optimal maintenance time window. Lastly, a case study is conducted, and the results indicate that the proposed maintenance strategy can increase wind power annual operating hours by more than 1%.

Hohai University (CN), Shanghai Electric (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 64%
Machine Fault Diagnosis Techniques
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