By Topic

Neural-based monitoring system for health assessment of electric transmission lines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sun-Wook Lim ; Power Syst. Eng. Res. Center, Colorado Sch. of Mines, Golden, CO, USA ; Shoureshi, R.A.

This paper presents a real-time monitoring system based on artificial neural network (ANN) to diagnose mechanical integrity of electric transmission lines. A sensor system composed of a receiver and a transmitter generates micro-acoustic waves along the conductor, and captures reflected waves. For key information, embedded in the captured signatures, feature extraction methods and ANN-based classifiers are considered and employed. In this monitoring system, fault detection is achieved using the reflected signatures from broken strands of transmission lines. Details of the system design, procedure and the results of performance studies in a laboratory testbed are presented. Based on laboratory experimental results, this system with both MLP (multilayer perceptron) and ART (adaptive resonance theory) classifiers show satisfactory performance in fault classification.

Published in:

American Control Conference, 2003. Proceedings of the 2003  (Volume:3 )

Date of Conference:

4-6 June 2003