A data-driven analytical model for wind turbine wakes using machine learning method

To reduce the wake effect by means of layout optimization or cooperative control, it is significant to modeling wind turbine wakes in an accurate and efficient way. However, existing analytical wake models still have large errors in actual wind farms due to the inadequate consideration of various inflow factors and local environmental characteristics. To satisfy this accuracy requirement, a data-driven analytical wake model is proposed in this paper. In the model, the local inflow information and wake expansion feature are extracted from measured data of wind farms, and a machine learning model is trained to establish the relationship between the two. In this way, the model can be well adapted to the local environment and inflow conditions. Verifications in two actual wind farm cases illustrate that there is a good agreement with the measured velocity and power data. Compared with traditional analytical models, the wake prediction performance of the new model has improved more than 20%. Therefore, the proposed model can serve as a reliable tool for wind farm control and optimization.

Automated summary

A data-driven analytical wake model is proposed in this paper, which extracts local inflow information and wake expansion features from measured data of wind farms, trains a machine learning model to establish the relationship between the two, and improves the wake prediction performance by over 20% compared to traditional analytical models.