Wind turbine power curve modeling method with Logistic functions based on quantile regression

The wind turbine power curve (WTPC) modeling is of great significance for wind power forecasting, condition monitoring, and performance assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Quantile regression logistic functions (QRLF) is embedded in the logistic function, and the QRLF model was established, so that the proposed method can describe the uncertainty of wind power. In order to reduce the effect of outliers in original data, an adaptive outlier filtering method is developed based on QRLF. Supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.