Analysis of Outage Frequency and Duration in Distribution Systems using Machine Learning

Abstract

Outage duration plays an important role in assessing the impacts of distribution system outages. Moreover, whenever an outage occurs, customers are most concerned about when power will be restored, i.e. the duration of the outage. Hence, this paper presents an analysis of the frequency and duration of outages using outage data from a distribution system network. In addition, this study performs a feature importance analysis by using random forests and gradient boosting regressors to determine which features in the outage dataset are most important in predicting the duration of an outage. The results show that climatic description, failed equipment and wind speed are the most important predictors of outage duration in the dataset used in this analysis.