Comparisons of logistic regression and artificial neural network on power distribution systems fault cause identification

Power distribution systems play an important role in modern society. Proper outage root cause identification is often essential for effective restorations when outages occur. This paper reports on the investigation and results of two classification methods: logistic regression and neural network app...

Full description

Saved in:
Bibliographic Details
Main Authors: Xu, L., Chow, M.-C., Gao, X.Z.
Format: Conference Proceeding
Language:English
Subjects:
Animals
Artificial neural networks
Circuit faults
Fault diagnosis
Logistics
Neural networks
Power distribution
Power distribution faults
Power system reliability
Power system restoration
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Power distribution systems play an important role in modern society. Proper outage root cause identification is often essential for effective restorations when outages occur. This paper reports on the investigation and results of two classification methods: logistic regression and neural network applied in power distribution fault cause classifier. 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 the classifier includes: correct classification rate, true positive rate, true negative rate, and geometric mean. Two major distribution faults, tree and animal contact, are used to illustrate the characteristics and effectiveness of the investigated techniques.
DOI:10.1109/SMCIA.2005.1466960