Modeling the impact of local environmental variables on tree-related power outages along distribution powerlines

Abstract

Accurate vegetation risk modeling requires detailed and timely information on the physical structure of roadside trees in conjunction with local environmental and management factors. This study aims to understand the contribution of multiple environmental and management variables on tree-related power outages during storm events at a fine scale. We developed and compared five candidate vegetation risk models (VRMs) comprising 19 predictor variables falling into four categories: (1) forest characteristics, (2) soil and terrain, (3) vegetation management, and (4) utility infrastructure using Random Forest (RF) algorithm. The VRM model consisting of all four categories of predictor variables demonstrated the highest area under the receiver operating characteristic curve (AUC-ROC) value of 0.8320. The VRM that excluded tree-related variables exhibited the lowest AUC-ROC value of 0.7760. Two VRMs excluding soil/terrain and vegetation management variables performed marginally worse than the model with all variables. According to Shapley Additive Explanation analysis, the LiDAR-derived tree variables (proximity tree pixels), length of primary distribution overhead, median tree height, and wire properties (i.e., covered or bare) reported the highest importance-variables. This study's findings can guide vegetation risk at fine scales, which will help improve electrical grid resilience and hardening.