For wind farm control, the current practice in industry is that every turbine has its own wind turbine controller that will optimize its own performance in terms of power output, load mitigation, and/or reference tracking. However, in several national American and European1 resea
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For wind farm control, the current practice in industry is that every turbine has its own wind turbine controller that will optimize its own performance in terms of power output, load mitigation, and/or reference tracking. However, in several national American and European1 research projects, researchers from industry and academia have shown that if you lower the power set point of the first turbine in a row of turbines you can increase the total amount of energy captured in that row by 2%. These control results were mainly obtained by using engineering models and simplistic scaled wind tunnel experiments. In more recent studies,2 it has been shown that the success of these methodologies highly depend on the atmospheric conditions, the quality of the model, and the variability of the flow within a wind farm. In this presentation, we will present a closed-loop control framework that can mitigate the inevitable uncertainties present in the control-oriented models and that is robust against the time-varying behaviour.@en
