Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

Zhang, Junqiang; Chowdhury, Souma; Zhang, Jie; Tong, Weiyang; Messac, Achille

doi:10.1002/we.2815

Cited by 3 publications

(2 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraint (1) specifies the maintenance crew capacity limit, which ensures that the PMs started concurrently cannot be larger than m. Constraints ( 2)-( 4) specify that once a PM is started at period t, then the PM will be conducted for a time length of h. Constraint (5) ensures that the PM cannot be conducted in those periods with bad weather conditions. Constraint (6) requires that any one PM for any wind turbine must be supplied by a maintenance vessel. Constraint (7) means that any conducted PM must be finished before the end period |T|.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Such studies contribute to the development of robust maintenance strategies and underscore the essential nature of maintenance in optimizing offshore wind farm performance [5]. To uphold system reliability above a crucial threshold and minimize unexpected failures, the implementation of routine preventive maintenances (PMs) is imperative [6]. These PMs serve to inspect, identify, and rectify potential component failures.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

View full text Add to dashboard Cite

Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.

show abstract

Section: Mathematical Modelingmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

Formulations and heuristic for the long-term preventive maintenance order scheduling problem

de Andrade,

Souza,

de Sá

et al. 2024

Computers & Operations Research

View full text Add to dashboard Cite

Effect of Rotor Faults on Wind Turbine Shutdown or Continued Operation

Biazar,

Khaloozadeh,

Siahi

2024

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

The dynamics of wind turbine (WT) behavior and the identification of factors influencing its performance are both highly complex and challenging. Additionally, component damage, along with sensor and actuator failures, can lead to system faults that significantly reduce performance. Understanding these impacts provides control system designers with valuable insights to develop strategies for mitigating faults and enhancing WT performance. This study presents a novel evaluation of rotor fault effects on output power and measured variables in WTs using Monte Carlo simulation and sensitivity analysis. Through comprehensive simulation and numerical analysis, the faults with the greatest impact on WT performance are identified. The results of this research not only provide a better understanding of the WT performance during faults but also identify the significant effects of these faults on the performance of wind turbines and specifically identify and prioritize the main faults. These results are instrumental in improving control strategies, developing preventive maintenance programs, and offering practical solutions to reduce operational costs and extend the equipment’s useful life.

show abstract

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

Cited by 3 publications

References 83 publications

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Formulations and heuristic for the long-term preventive maintenance order scheduling problem

Effect of Rotor Faults on Wind Turbine Shutdown or Continued Operation

Contact Info

Product

Resources

About