Power distribution systems fault cause identification using logistic regression and artificial neural network

Power distribution systems play an important role in modern society. When outages occur, fast and proper restorations are crucial to improve system reliability. Proper outage root cause identification is often essential for effective restorations. This paper reports on the investigation of two classification methods: logistic regression and neural network, applied in power distribution fault cause classifier (PDFCC) and comparison of their results. Logistic regression is seldom used in power distribution fault diagnosis, while neural network has been extensively used in power system reliability researches. Evaluation criteria of the goodness of PDFCC includes: correct classification rate, true positive rate, true negative rate, and geometric mean. This paper also discusses the practical application issues including data insufficiency, imbalanced data constitution, and threshold setting that are often faced in power distribution fault diagnosis. Two major distribution faults, 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. Their proper operations keep industrial productions on track and people's lives in order. However, power distribution systems are geographically dispersed and under various dynamic operating environments, they can be significantly affected by various faults, such as equipment failure, animal contacts, trees, lightning, etc. These faults often lead to power outages.