Automated daily maintenance planning for offshore wind farms
article
Offshore wind farm managers and schedulers need to manage large numbers of wind turbine visits every
day, in order to: repair minor faults; conduct inspections; and perform scheduled service operations.
Daily schedules form a choice of which maintenance activities to conduct, taking account of: constraints
on weather conditions, shifts, vessel and technician capabilities and availability; and the impact of activities
on wind farm profitability. This forms a formidable optimisation challenge that today is solved “by
hand” by a scheduler.
The work presented here contains three aspects of importance. First, a powerful and flexible metaheuristic
optimisation model is developed to solve this problem, where the simulation algorithms and
objective can be altered without any change to the optimiser. Second, a practical valuation methodology
is developed, where historic wind farm data can be used to identify strengths and weaknesses in any
maintenance planning method and estimate financial return on investment from implementation.
Finally, the methodology described is implemented and tested, by applying the valuation methodology to
data from the Princess Amalia Wind Park in The Netherlands. Even given the limited scope of this case
study, automating daily maintenance planning can bring significant financial benefits: 302 kV over 5
months
day, in order to: repair minor faults; conduct inspections; and perform scheduled service operations.
Daily schedules form a choice of which maintenance activities to conduct, taking account of: constraints
on weather conditions, shifts, vessel and technician capabilities and availability; and the impact of activities
on wind farm profitability. This forms a formidable optimisation challenge that today is solved “by
hand” by a scheduler.
The work presented here contains three aspects of importance. First, a powerful and flexible metaheuristic
optimisation model is developed to solve this problem, where the simulation algorithms and
objective can be altered without any change to the optimiser. Second, a practical valuation methodology
is developed, where historic wind farm data can be used to identify strengths and weaknesses in any
maintenance planning method and estimate financial return on investment from implementation.
Finally, the methodology described is implemented and tested, by applying the valuation methodology to
data from the Princess Amalia Wind Park in The Netherlands. Even given the limited scope of this case
study, automating daily maintenance planning can bring significant financial benefits: 302 kV over 5
months
TNO Identifier
874874
Source
Renewable Energy, 133, pp. 1393-1403.
Pages
1393-1403
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