LAUSR

In industrial-scale wind farms, interactions among multiple turbines alter the power generation efficiency of turbines. In general, turbines in downwind rows are impacted by wind deficits, producing less power, compared to upstream turbines. Therefore, the generation performance of multiple turbines differs from one another. Moreover, the power curve of each turbine becomes heterogeneous when changes in wind direction cause some upstream turbines to become downstream turbines. This study proposes an integrative methodology that quantifies the heterogeneous wake effects over a range of wind direction by utilizing the concept of canonical models and similarity functions. We model the direction-dependent multi-turbine power curves in a Bayesian hierarchical framework and validate the performance of the proposed approach with multiple real-world data sets from industrial-scale wind farms.