A learning-based outage prediction method for resilient electricity distribution systems in response to extreme weather events

Abstract

Electricity distribution systems are vulnerable to damage from extreme weather events like hurricanes and floods. Although predicting outage locations in these systems is a significant challenge, it provides operators with critical data for implementing proactive measures. This paper presents a decision tree-based learning method to predict potential outages in distribution branches during hurricanes. The challenges of input data, component diversity, and numerous affecting factors are highlighted and effectively addressed. Our model considers all potentially effective static and dynamic features to estimate the damage risk for each branch. The data for training and testing the classifier were acquired from historical records and synthesized samples based on expert knowledge, with a separate set of real data used for validation. Beyond outage prediction, the classifier also serves as a feature selection tool by identifying the most discriminative features. Numerical simulations confirmed a high level of accuracy with a negligible error rate. The method was successfully implemented on a modified IEEE 33-bus distribution system. Copyright © 2025. Published by Elsevier Ltd.