Skip to main navigation
Skip to search
Skip to main content
University of Kentucky Home
LOGIN & Help
Home
Research units
Researchers
Grants & Contracts
Research output
Facilities & Equipment
Honors & Awards
Activities
Search by expertise, name or affiliation
Fault Identification on Electrical Transmission Lines Using Artificial Neural Networks
Chris Asbery,
Yuan Liao
Power and Energy Institute of Kentucky (PEIK)
Electrical and Computer Engineering
Energy Research Priority Area
College of Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Fault Identification on Electrical Transmission Lines Using Artificial Neural Networks'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Engineering
Electric Lines
100%
Classification
60%
Artificial Neural Network
40%
Determines
20%
Measurement
20%
Electric Potential
20%
Customers
20%
Events
20%
Electric Power Transmission
20%
System Reliability
20%
Line Fault
20%
Power Flow
20%
Substations
20%
Outage Time
20%
Computer Science
Transmission Line
100%
Classification
60%
Artificial Neural Network
40%
Identification
20%
Events
20%
Longer Distance
20%
Fault Location
20%
Neural Network Architecture
20%
Transmission System
20%
Physics
Position (Location)
40%
Artificial Neural Network
40%
Events
20%
Electric Potential
20%
Fault Location
20%