A Bayesian Hierarchical Model to Understand the Effect of Terrain on Wind Turbine Power Curves

This paper is concerned with explicitly modeling the effect of terrain on wind power curves. Terrain characteristics are spatially-varying but temporally constant, whereas other power curve-affiliating variables such wind speed, temperature, and wind power vary both spatially and temporally. In order to effectively model such two modes of variation in the data, we employ a Bayesian hierarchical model (BHM) that connects the terrain characteristics with the parameters in a power curve. BHM jointly models the data from all turbines on a wind farm for attaining the turbine-specific, terrain-incorporating power curves. Our analysis shows that, out of the three terrain variables available in our data, ruggedness has the strongest effect on the power curve. We also evaluate the applicability of using the resulting power curve model for turbines on a different terrain and find that incorporating terrain information explicitly is beneficial. The specific BHM mechanism of using terrain information leads to 7–10% improvement over the group averaging approach.