A data-driven approach for cooperative wind farm control

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This paper discusses a data-driven, cooperative control strategy to maximize wind farm power production. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions among the wind turbines in a wind farm. Such greedy control strategy, when an upstream wind turbine attempts to maximize its power production, can significantly lower the power productions of the downstream wind turbines and, thus, reduces the overall wind farm power production. As an alternative, we propose a cooperative wind farm control strategy that determines and executes the optimum coordinated control actions that maximize the total wind farm power production. To determine the optimum coordinated control actions of the wind turbines, we employ Bayesian Ascent (BA), a probabilistic optimization method constructed based on Gaussian Process regression and the trust region concept. Wind tunnel experiments using 6 scaled wind turbine models are conducted to assess (1) the effectiveness of the cooperative control strategy in improving the power production, and (2) the efficiency of the BA algorithm in determining the optimum control actions of the wind turbines using only the input control actions and the output power measurement data.