Open AccessDecision tree‐based classifiers for root‐cause detection of equipment‐related distribution power system outages
Author(s) -
Dehbozorgi Mohammad Reza,
Rastegar Mohammad,
Dabbaghjamanesh Morteza
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0570
Subject(s) - reliability (semiconductor) , decision tree , computer science , reliability engineering , binary decision diagram , power (physics) , tree (set theory) , electric power system , data mining , artificial intelligence , engineering , mathematics , algorithm , mathematical analysis , physics , quantum mechanics
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.