Cost optimization of maintenance scheduling for wind turbines with aging components
Doctoral thesis, 2021
A major part of the wind turbine operation cost is resulted from the maintenance of its components. This thesis deals with the theory, algorithms, and applications concerning minimization of the maintenance cost of wind power turbines, using mathematical modelling to find the optimal schedules of preventive maintenance activities for multi-component systems.
The main contributions of this thesis are covered by the four papers appended. The unifying goal of these papers is to produce new optimization models resulting in effective and fast algorithms for preventive maintenance time schedules. The features of the multi-component systems addressed in our project are: aging components, long-term, and short-term planning, planning for a wind power farm, end of the lifetime of the wind farm, maintenance contracts, and condition monitoring data.
For the long-term maintenance planning problem, this thesis contains an optimization framework that recognizes different phases of the wind turbine lifetime. For short-term planning problem, this thesis contains two modeling frameworks, which both focus on the planning of the next preventive maintenance activities. Our virtual experiments show that the developed optimization models adopt realistic assumptions and can be accurately solved in seconds. One of these two frameworks is further extended so that available condition monitoring data can be incorporated for regular updates of the components' hazard functions. In collaboration with the Swedish Wind Power Technology Center at Chalmers and its member companies, we test this method with real-world wind farm data. Our case studies demonstrate that this framework may result in remarkable savings due to the smart scheduling of preventive maintenance activities by monitoring the ages of the components as well as operation data of the wind turbines.
We believe that in the future, the proposed optimization model for short-term planning based on the component age and condition monitoring data can be used as a key module in a maintenance scheduling app.
The main contributions of this thesis are covered by the four papers appended. The unifying goal of these papers is to produce new optimization models resulting in effective and fast algorithms for preventive maintenance time schedules. The features of the multi-component systems addressed in our project are: aging components, long-term, and short-term planning, planning for a wind power farm, end of the lifetime of the wind farm, maintenance contracts, and condition monitoring data.
For the long-term maintenance planning problem, this thesis contains an optimization framework that recognizes different phases of the wind turbine lifetime. For short-term planning problem, this thesis contains two modeling frameworks, which both focus on the planning of the next preventive maintenance activities. Our virtual experiments show that the developed optimization models adopt realistic assumptions and can be accurately solved in seconds. One of these two frameworks is further extended so that available condition monitoring data can be incorporated for regular updates of the components' hazard functions. In collaboration with the Swedish Wind Power Technology Center at Chalmers and its member companies, we test this method with real-world wind farm data. Our case studies demonstrate that this framework may result in remarkable savings due to the smart scheduling of preventive maintenance activities by monitoring the ages of the components as well as operation data of the wind turbines.
We believe that in the future, the proposed optimization model for short-term planning based on the component age and condition monitoring data can be used as a key module in a maintenance scheduling app.
Integer linear optimization
Age-based preventive maintenance scheduling
Renewal-reward theorem
Weibull distribution
Combinatorial optimization
Wind turbine maintenance
Virtual replacement
Condition monitoring data
Cox proportional hazards method
Linear programming
Pascal, Chalmers tvärgata 3, Chalmers.
Opponent: Professor Henrik Andersson, Department of Industrial Economics and Technology Management, NTNU, Trondheim, Norway
Author
Quanjiang Yu
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Optimal scheduling of the next preventive maintenance activity for a wind farm
Wind Energy Science,;Vol. 6(2021)p. 949-959
Journal article
Mathematical optimization models for long-term maintenance scheduling of wind power systems
Preprint
Optimal preventive maintenance schedule for a wind turbine with aging components
Algorithms,;Vol. 16(2023)
Journal article
Optimal preventive maintenance scheduling for wind turbines under condition monitoring
Energies,;Vol. 17(2024)
Journal article
A large part of the operational cost of a wind turbine, especially offshore wind turbines, is caused by equipment maintenance. Compared to the traditional way of maintenance (e.g. repairing or replacing the components after they break down), preventive maintenance, as an alternative, has several advantages. A preventive maintenance action is performed before a breakdown of the component, so that the timing of such action involves optimization under uncertainty. In this dissertation, the author develops various optimization models aiming at preventive maintenance scheduling and minimizing the maintenance cost of a wind farm.
The proposed optimization algorithms were tested in different computational case studies. According to the results, the cost can be considerably reduced––(i.e. more than 20%) ––by the optimal scheduling of preventive maintenance activities.
In addition, it shows that the developed optimization model is highly computationally efficient, holding the potential to be used as a key ingredient of a future maintenance scheduling app. Such an app could use the condition of various wind turbine components of a wind farm as the input information, and output (1) suggestions on the optimal time for the next preventive maintenance activity, and (2) the exact components that should be attended in this maintenance activity.
The proposed optimization algorithms were tested in different computational case studies. According to the results, the cost can be considerably reduced––(i.e. more than 20%) ––by the optimal scheduling of preventive maintenance activities.
In addition, it shows that the developed optimization model is highly computationally efficient, holding the potential to be used as a key ingredient of a future maintenance scheduling app. Such an app could use the condition of various wind turbine components of a wind farm as the input information, and output (1) suggestions on the optimal time for the next preventive maintenance activity, and (2) the exact components that should be attended in this maintenance activity.
Development of mathematical optimization models and methods towards a successful integration of production and condition-based multi-component maintenance in the wind power industry
Swedish Research Council (VR) (2014-5138), 2015-01-01 -- 2018-12-31.
Site-Adaptive Analysis Methods to Predict and Enhance Lifetime of Wind Turbines
Swedish Wind Power Technology Center (SWPTC), 2019-07-01 -- 2022-12-31.
Driving Forces
Sustainable development
Subject Categories
Computational Mathematics
Reliability and Maintenance
Areas of Advance
Energy
ISBN
978-91-7905-484-7
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4951
Publisher
Chalmers
Pascal, Chalmers tvärgata 3, Chalmers.
Opponent: Professor Henrik Andersson, Department of Industrial Economics and Technology Management, NTNU, Trondheim, Norway