A Semi-decentralized Data-Model-Driven Optimization Scheme for Coordinated Control of Large-Scale Wind Farm Power Maximization

The wake effect, which significantly degrades the power output of a wind farm, has prompted widespread consideration of the farm-level power generation optimization problem. However, the optimization problem is highly challenging when a large-scale wind farm encounters highly dynamic wind conditions. To address this issue, we proposed a semi-decentralized data-model-driven optimization scheme that utilizes real-time data by interacting with the wind farm and has a fast solving speed. First, the optimization problem is divided into several static subproblems according to the power efficiency in different wind directions. For every static subproblem, the large-scale farm is decomposed into clusters based on the wake model and the spectral clustering algorithm. When controlled online, data-driven methods are performed in parallel for power maximization according to the cluster division result. In addition, a multivariable coupling wake model is employed to validate the effectiveness of the optimization scheme. Simulation results indicate an apparent enhancement in energy output for large-scale wind farms operating under dynamic wind conditions.