  This paper implements a recently developed social hierarchy-based distributed
economic model predictive control (DEMPC) algorithm in floating offshore wind
farms for the purpose of power maximization. The controller achieves this
objective using the concept of yaw and induction-based turbine repositioning
(YITuR), which minimizes the overlap areas between adjacent floating wind
turbine rotors in real-time to minimize the wake effect. Floating wind farm
dynamics and performance are predicted numerically using FOWFSim-Dyn. To ensure
fast decision-making by the DEMPC algorithm, feed-forward neural networks are
used to estimate floating wind turbine dynamics during the process of dynamic
optimization. For simulated wind farms with layouts ranging from 1-by-2 to
1-by-5, an increase of 20% in energy production is predicted when using YITuR
instead of greedy operation. Increased variability in wind speed and direction
is also studied and is shown to diminish controller performance due to rising
errors in neural network predictions.
