Research on a Comprehensive Maintenance Optimization Strategy for an Offshore Wind Farm

Lu, Yang; Sun, Liping; Yang, Xue

doi:10.3390/en14040965

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…Novel maintenance strategies have been constantly proposed to increase the reliability of turbines and reduce cost. [5][6][7] Opportunistic maintenance is one of the emerging concepts, which groups different types of maintenance activities of multiple turbines together to reduce costs. [8][9][10][11][12] Life cycle cost is an important measure used to assess the efficiency of turbine maintenance strategies.…”

Section: Operation and Maintenance Of Offshore Wind Farmsmentioning

confidence: 99%

“…A variety of decision support models have shown that O&M have a great influence on the economy of wind projects. Novel maintenance strategies have been constantly proposed to increase the reliability of turbines and reduce cost 5–7 . Opportunistic maintenance is one of the emerging concepts, which groups different types of maintenance activities of multiple turbines together to reduce costs 8–12 .…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Chowdhury,

Zhang

et al. 2023

Wind Energy

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The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed‐integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility‐scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.

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Section: Operation and Maintenance Of Offshore Wind Farmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Chowdhury,

Zhang

et al. 2023

Wind Energy

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“…The selection of an appropriate maintenance strategy plays a pivotal role in the normal functioning of wind farms. Optimal maintenance strategies depend on various factors, including energy costs, required reliability levels, weather conditions, availability of skilled technical personnel, and the accessibility of crew transport vehicles [35][36][37]. Maintenance strategies may vary across different power plants based on factors such as the type, size, and number of wind turbines, required reliability, and other maintenance standards [38].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

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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.

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“…Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs [4][5][6]. The location of a wind farm is very important to the feasibility of wind turbine investment, and it is also related to environmental impacts such as wildlife and socio-economic factors.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China

Cui

et al. 2021

Energies

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Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations.

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Research on a Comprehensive Maintenance Optimization Strategy for an Offshore Wind Farm

Cited by 6 publications

References 19 publications

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

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

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

Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China

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