LAUSR

ABSTRACT Modern utility-scale wind farms consist of a large number of wind turbines. In order to improve the power generation efficiency of wind turbines, accurate quantification of power generation levels of multi-turbines is critical, in both wind farm design and operational controls. One challenging issue is that the power output levels of multiple wind turbines are different, due to complex interactions between turbines, known as wake effects. In general, upstream turbines in a wind farm absorb kinetic energy from wind. Therefore, downstream turbines tend to produce less power than upstream turbines. Moreover, depending on weather conditions, the power deficits of downstream turbines exhibit heterogeneous patterns. This study proposes a new statistical approach to characterize heterogeneous wake effects. The proposed approach decomposes the power outputs into the average pattern commonly exhibited by all turbines and the turbine-to-turbine variability caused by multi-turbine interactions. To capture the wake effects, turbine-specific regression parameters are modeled using a Gaussian Markov random field. A case study using actual wind farm data demonstrates the proposed approach's superior performance.