Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression

Featured Application The research proposed in this paper could be useful in wind power forecasting, condition monitoring and energy assessment of wind turbine. The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, 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.

Automated summary

The research introduces a novel WTPC modeling method with logistic functions based on quantile regression, which can better describe the uncertainty of wind power and exhibit superior fitting performance compared to typical models. The method combines asymmetric absolute value functions with logistic functions, and includes an adaptive outlier filtering method.