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This paper implements a recently developed social hierarchy-based distributedeconomic model predictive control (DEMPC) algorithm in floating offshore windfarms for the purpose of power maximization. The controller achieves thisobjective using the concept of yaw and induction-based turbine repositioning(YITuR), which minimizes the overlap areas between adjacent floating windturbine rotors in real-time to minimize the wake effect. Floating wind farmdynamics and performance are predicted numerically using FOWFSim-Dyn. To ensurefast decision-making by the DEMPC algorithm, feed-forward neural networks areused to estimate floating wind turbine dynamics during the process of dynamicoptimization. For simulated wind farms with layouts ranging from 1-by-2 to1-by-5, an increase of 20% in energy production is predicted when using YITuRinstead of greedy operation. Increased variability in wind speed and directionis also studied and is shown to diminish controller performance due to risingerrors in neural network predictions.

Social Hierarchy-based Distributed Economic Model Predictive Control of Floating Offshore Wind Farms