@misc{Requate_2021,
  howpublished = {Presentation},
  type={Conference presentation},
  abstract = {Energy production inevitably invokes fatigue loads in wind turbine components. Even though site specific wind conditions are included in the design process, the actual fatigue loads of a turbine will differ from the design conditions. This might be due to differing wind conditions, partial overloading or wake effects in a wind farm . When the total fatigue budget of a turbine would be exceeded due to one of the reasons, derating the turbine can be applied to spare the system (Astrain Juangarcia et al. 2018). When reliability control strategies are used, adapting the operating controller of a turbine even becomes a useful mechanism to influence the fatigue contribution in each situation as desired. Then, the turbine can be controlled over its entire lifetime, so that the fatigue budget of the turbine is used up at the end of its lifetime (Requate und Meyer 2020). 

In all cases, the available fatigue budget must be distributed over the remaining lifetime of the turbine in a strategic way. However, pre-planning a specific fatigue budget contribution for a certain time span in the future cannot take the-current wind conditions into account. Instead, we propose to use a wind-condition based fatigue budget distribution. This way, load-reducing derating is employed where it is most efficient, and fatigue load reduction is achieved with lowest energy loss. The objective then becomes the maximization of lifetime energy yield while satisfying the constraint of using up the available fatigue budget at the end of life. However, since a derating parameter can be set for all wind conditions individually, the optimization problem is of very high order and cannot realistically be solved.

Dimensionality of the optimization problem can be reduced by incorporating additional engineering knowledge. This is done by using the energy-related damage contribution r(w) = D(w)/E(w)where w are the parameters of the incoming ambient wind conditions. D(w)is the fatigue damage contribution and E(w) is the energy contribution. The aim then becomes to adjust power output and fatigue loads for wind conditions where r(w) is highest. So instead of adjusting derating parameters for all wind conditions individually, we only adjust where it is most effective. Thus, we use a derating factor c(r), which is dependent on the energy-related damage contribution. The optimal fatigue reduction process is outlined in Figure 1. As example, flapwise blade root bending moment of a single turbine inside an example wind farm is used. The derating is mainly applied when the wind is coming from north or south. Here, the turbine operates in the wake of a second turbine. By applying this method, the total fatigue damage can be nearly halved over the total lifetime while the reduction in total energy is only 1.5{\%}. 

Using this approach provides an optimal derating strategy based on the wind situation and the individual efficiency of derating. As a subsequent step, also uprating of turbines can be considered. In addition, the influence of derating strategies on different components needs to be included as well as the interaction in a wind farm due to changed wake effect. The strategy can also be used to create setpoints for a reliability controller which can follow the desired fatigue distribution while allowing for deviations when it is purposeful, e.g., when the price of electricity is high.},
  author = {Requate, Niklas and Meyer, Tobias},
  year = {2021},
  date = {2021-05-26},
  title = {From wind conditions to derating strategy:  Energy-optimal derating strategy for lifetime fatigue damage planning based on energy-related damage contribution across the incoming wind conditions},
  address = {online},
  note = {Slides available at: \url{http://www.tobi-meyer.de/Requate_2021.pdf}},
  series = {Wind Energy Science Conference}
}