Abstract

This paper proposes an algorithm to maximize the power extraction in wind turbine arrays under varying wind. Wind turbine arrays can be viewed as large coupled networks, for which the application of traditional optimization techniques are impractical. In this paper we present an extension to a dynamic programming solution previously developed under uniform wind and extend it to higher-fidelity wind models. We then update our solution for dynamically evolving wind conditions. Using a Markov model derived from real-world data, the underlying optimization problem is reformulated in a Model Predictive Control framework. Simulation results are discussed, which demonstrate our algorithm provides improved performance compared to prior results.

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