Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses
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 pa...
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| Published in | Wind energy (Chichester, England) Vol. 26; no. 11; pp. 1103 - 1122 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
John Wiley & Sons, Inc
01.11.2023
Wiley |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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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| AbstractList | 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. Abstract 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. |
| Author | Zhang, Junqiang Tong, Weiyang Zhang, Jie Messac, Achille Chowdhury, Souma |
| Author_xml | – sequence: 1 givenname: Junqiang orcidid: 0000-0002-2744-0124 surname: Zhang fullname: Zhang, Junqiang organization: School of Artificial Intelligence Hezhou University Hezhou Guangxi China – sequence: 2 givenname: Souma surname: Chowdhury fullname: Chowdhury, Souma organization: Department of Mechanical and Aerospace Engineering University at Buffalo Buffalo New York USA – sequence: 3 givenname: Jie surname: Zhang fullname: Zhang, Jie organization: Department of Mechanical Engineering University of Texas at Dallas Richardson Texas USA – sequence: 4 givenname: Weiyang surname: Tong fullname: Tong, Weiyang organization: Department of Mechanical and Aerospace Engineering Syracuse University Syracuse New York USA – sequence: 5 givenname: Achille surname: Messac fullname: Messac, Achille organization: Department of Mechanical Engineering Howard University Washington DC USA |
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| SubjectTerms | Climatic data Downtime Economics Energy Energy loss Genetic algorithms Offshore Offshore energy sources offshore wind farm Optimization Preventive maintenance Profitability Schedules scheduling Turbines wake effects Weather Wind effects Wind farms Wind power Wind power generation Wind speed Wind turbines Windows (intervals) |
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| Title | Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses |
| URI | https://www.proquest.com/docview/2877561485 https://doaj.org/article/90f2816b8db545d9befc734d0bb2a22e |
| Volume | 26 |
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