Outage Cause Detection in Power Distribution Systems Based on Data Mining

Abstract

Realizing the factors involved in power system outages can be effective in reliability improvement. In this article, we analyze the distribution power network outage data to find dominant factors in occurring vegetation-, animal-, and equipment-related outages. After their integration, real outage, weather, and load as the input data are used to extract associated features. In this article, visualization techniques are initially utilized to show the impact of features on the outage occurrence and then association rule mining is used to find factors correlated with each outage type as well as each other. Association rules are mined using Apriori technique, considering the chi-square and lift index as the measures of interestingness. The outage analyses are also performed for each equipment separately to find the associated rules. The results showing the effectiveness and validity of the proposed method to identify the factors connected with outage occurrences can be used for future planning and the operation schedule of distribution power networks.