Modeling Weather-Related Failures of Overhead Distribution Lines

Abstract

Weather is one of the major factors affecting the reliability of power distribution systems. An effective method to model weather's impact on overhead distribution lines' failure rates will enable utilities to compare their systems' reliabilities under different weather conditions. This will allow them to make the right decisions to obtain the best operation and maintenance plan to reduce impacts of weather on reliabilities. Two methods to model overhead distribution lines' failure rates are presented in this paper. The first is based on a Poisson regression model, and it captures the counting nature of failure events on overhead distribution lines. The second is a Bayesian network model, which uses conditional probabilities of failures given different weather states. Both methods are used to predict the yearly weather-related failure events on overhead lines. This is followed by a Monte Carlo analysis to determine prediction bounds. The results obtained by these models are compared to evaluate their salient features.