Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes

Wind turbine power curves are often used for monitoring the performance of wind turbines and play an important role in wind power forecasting. Various factors such as age of turbines, installed location, air density, wind direction and measurement errors cause non-homogeneity among observed data, which often influences the accuracy of fitted power curves. To overcome this problem, a hybrid estimation approach is proposed, based on weighted balanced loss functions that account for both estimation error and goodness of fit by shrinking estimates toward standardized target models. Two different weighting schemes are developed to incorporate the non-homogeneity of data in the estimation process. The proposed algorithm is compared with commonly used power curve estimation methods based on classical least squares, natural splines, and local linear smoothing methods. The performance of the original and proposed hybrid methods is evaluated using a historical data set collected from a wind farm located in Canada. Results show that hybrid methods can be used as an effective tool to improve the performance of existing power curve modeling approaches. Consequently, proposed methods can facilitate more robust monitoring the performance of wind turbines as well as wind power forecasting.