Wake effect parameter calibration with large-scale field operational data using stochastic optimization

This study aims to show the application of stochastic optimization for efficient and robust parameter calibration of engineering wake models. Standard values of the wake effect parameters are generally used to predict power using engineering wake models, but some recent studies have shown that these values do not result in accurate prediction. The proposed approach estimates the wake effect parameters using operational data available from actual wind farms to minimize the prediction error of the wake model by using trust-region optimization. To further improve computational efficiency, we implement stratified adaptive sampling. We employ decision trees to stratify the data and propose two ways of adapting the sampling budget to the constructed strata: budget allocation with dynamic weights and fixed weights. We extend our analysis to determine the functional relationship between the turbulence intensity and wake decay coefficient. Our experiments suggest that wake parameters or a functional relationship between turbulence intensity and wake decay coefficient may need adjustments (from assumed standard values) for a particular wind farm using its operational data to characterize the wake effect better.