Machine-learning-based wind farm optimization through layout design and yaw control

Abstract

The high power extraction of wind farms necessitates a comprehensive optimization system encompassing precise and efficient layout design and cooperative control. This study applies a novel ANN-based power prediction framework to resolve the layout optimization combined with the genetic algorithm, then composes a double-layer machine learning framework for the control optimization incorporating Bayesian machine learning. Three wind scenarios and control schemes are accounted for to investigate their impacts on the actual power benefits of the layout and control optimizations, respectively. The study shows that the divergence of wind resources will lead to more evenly scattered turbine arrangements for each direction sector in the optimized layout but may weaken the functionality of layout design in annual power rise owing to the compromise between different wind directions. Under the spread wind scenario, the spread optimized layout may narrow the space for the power enhancement of the yaw control, whereas the unidirectional optimized layout may give rise to a more considerable power gain from the yaw control. Row-based control shows desirable performance in the regular aligned layout, while column-based control is recommended for irregularly optimized layouts, particularly for unidirectional optimized ones. The spread optimized layout will widen the gap of power improvement between the column-based and independent controls, thus weakening the advantage of the column-based control.