Power optimization for wind turbines based on stacking model and pitch angle adjustment

Abstract

As we know, power optimization for wind turbines has great significance in the area of wind power generation, which means to make use of wind resources more efficiently. Especially nowadays, wind power generation has become more and more important. Generally speaking, many parameters could be optimized to enhance power output, including blade pitch angle, which is usually ignored. In this article, a stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBOOST) and Light Gradient Boosting Machine (LGBM) is trained based on historical data exported from the Supervisory Control and Data Acquisition (SCADA) system for output power prediction. Then, we carry out power optimization through pitch angle adjustment based on the obtained prediction model. Our research results indicate that power output could be enhanced by adjusting pitch angle appropriately.