Smart cooperative control scheme for large-scale wind farms based on a double-layer machine learning framework

Abstract

In the real-time cooperative control of large-scale wind farms, the simultaneous achievement of accuracy and efficiency by the optimization framework plays an indispensable role. This paper presents a new double-layer machine learning (ML) framework comprising an Artificial Neural Networks (ANN) yawed wake model and Bayesian ML algorithm to strike a desirable compromise between accuracy and efficiency. Given the control on the iteration number with the scale-up of the wind farm, a novel row-based control scheme is further put forward to improve the optimization rate by reasonably reducing the optimization parameters. Moreover, parametric analysis has been performed considering the wind distribution and layout configuration to explore its applicability compared with the general independent one. The study shows that the novel framework performs favorably in an accurate and efficient power prediction and optimization of the wind farm. The row-based control scheme can further improve the convergence rate of the double-layer optimization framework remarkably at the expense of a slight decrease in optimal power production. The divergence of the wind distribution can dwindle the power gain of the wake steering strategy and weaken the superiority of the row-based cooperative control scheme. The row-based cooperative control scheme is more applicable to the aligned layout than the staggered layout, and this advantage is enhanced with the increase of wind farm scale.