LAUSR

Electric grids are vulnerable to the impacts of extreme weather. Utility companies face the necessity to reduce the number of power outages caused by weather. This paper expands the approach of predicting weather outages in the distribution grid by incorporating wind modeling. The models for the grid outage State of Risk (SoR) prediction are used by utilities to mitigate potential impacts and reduce outage durations. We study the performance of such models when they are enhanced by incorporating data from Wide Area Fine Grid Wind Modeling (WAFGWM). For a given period, WAFGWM produces wind fields that characterize the direction and speed of the wind over the area of interest. The process of extracting features for the Machine Learning (ML) algorithm is described. The new solution is tested utilizing actual grid performance data from a utility company. The results from nested cross-validation obtained on three years of data reveal that the proposed method improves model performance.