Decision tree-based classifiers for root-cause detection of equipment-related distribution power system outages

Recently, power system reliability has been challenged due to the increment of electrical demand. When an outage occurs, locating the outage may take a long time because of the distribution system's radial structure and the presence of various elements. To decrease the outage detection time, this study proposes to classify the equipment-related outage causes to diagnose the faulty equipment at the time of outage occurrence. To this end, available historical outage, load and weather data sets are integrated, and various features are defined. Then, binary classifiers are developed to classify each equipment's failures against others'. To enhance classifiers' performance, this study also proposes to use cost function and ensemble models. The results of applying proposed classifiers show the accuracy of the proposed method and improvements in outcomes.