Which meteorological parameters influence extreme wind speed in a wind farm? A heterogeneous Granger causality approach

Abstract For efficient wind farm management and optimized power generation under adverse weather conditions, understanding the causal meteorological drivers is essential. In this paper, we investigate the temporal causal influences of wind speed-related meteorological processes within a wind farm using the Heterogeneous Graphical Granger model (HMML). HMML is applied to synthetically generated wind power production data from Eastern Austria. To assess the plausibility of the identified causal processes, we compare the results with those obtained using the state-of-the-art LiNGAM method. Both methods are applied and evaluated across six different scenarios, each defined by distinct hydrological periods. The scenarios are defined by a set of time intervals characterized by either low/high extreme wind speeds or moderate wind speeds. We applied both methods across these scenarios and conducted causal reasoning to identify potential causes of extreme wind speeds within the wind farm. The sets of causal parameters obtained using HMML were found to be more realistic than those derived from LiNGAM. Combining the knowledge of causal variables affecting wind speed at the t