The following building blocks are defined for wind farm control:
The control technology defines how the control algorithm interacts with the physical system (e.g. pitch actuation, yaw actuation, lidar measurements). Important aspects here are: controllability, observability, loads, bandwidth, costs and availability
The (multi-)objective for which wind farm is employed (e.g. loads, power, predictability) and how this can be mathematically quantified. Important aspects are: convexity, constraints, scalability and predictability.
The model used for decision making e.g. engineering model, empirical model. Important aspects are: accuracy, computational load, coding language, included physics, disturbance
modelling and fidelity.
The decision making typically depends on the fusion between models and data collected by the sensors. Algorithms have to be developed that bring these two together. Important aspects are: accuracy, sampling time, sensors required, type of model knowledge required and convergence speed.
The objective function conditioned by the internal model will be used to make decisions on how actuators are employed over time. The important aspects are: reliability, convergence, convexity, adaptability, robustness, ability to work with uncertainty, and computational complexity.
The overview of the different building blocks will give a full landscape of the required software. Different combinations can be made of the different building block leading to a full landscape of the solution space.
In this work package an overview will be given of this landscape with a focus on the scalability and the complexity/performance trade-off.
Work Package 3 Leads:
Jan-Willem van Wingerden, Mikel Iribas Latour, Maria Aparicio Sanchez and Irene Equinoa Erdozain