Decentralized Data-Driven Power Optimization for Large-Scale Wind Farm Using Simultaneous Perturbation Stochastic Approximation

In a wind farm, cooperative turbine control is crucial for mitigating wake interactions between turbines and significantly improving the overall power output. However, accurately modeling wake interactions is very challenging for complex-terrain wind farms due to the complexity of the interactions. Many data-driven wind farm power optimization methods have been developed. These methods however typically require large amounts of real-time measurements due to their slow convergence, resulting in lower power performance, especially for large-scale wind farms. This paper proposes a decentralized data-driven wind farm power optimization method using simultaneous perturbation stochastic approximation. The presented method has fast convergence and thus can obviously enhance the power output of large-scale wind farm only by real-time power generation data. It does not need any extra communication channels between turbines (required by distributed algorithms) that is not always possible or desirable in practice. Furthermore, the method can adapt to changing turbine configurations due to decentralized design and is capable of addressing time-varying wind conditions through a hierarchical framework. Simulation tests demonstrate the effectiveness of the proposed method for the power optimization of the large-scale wind farm.