A classification approach for power distribution systems fault cause identification

Power distribution systems play an important role in modern society. When distribution system outages occur, fast and proper restorations are crucial to improve the quality of services and customer satisfaction. Proper usages of outage root cause identification tools are often essential for effective outage restorations. This paper reports on the investigation and results of two popular classification methods: logistic regression (LR) and artificial neural network (ANN) applied on power distribution fault cause identification. LR is seldom used in power distribution fault diagnosis, while ANN has been extensively used in power system reliability researches. This paper discusses the practical application problems, including data insufficiency, imbalanced data constitution, and threshold setting that are often faced in power distribution fault cause identification problems. Two major distribution fault types, tree and animal contact, are used to illustrate the characteristics and effectiveness of the investigated techniques.

I. Introduction

As the retail part of utilities, power distribution systems aim at providing reliable, economical, and safe supply of electricity to the users. However, power distribution systems are geographically dispersed, and under various dynamic operating environments, they can be significantly affected by various faults in terms of power reliability and quality of services, such as equipment failure, animal contacts, trees, lightning, etc.