Title :
ANNs pinpoint underground distribution faults
Author :
Glinkowski, M.T. ; Wang, N.C.
Author_Institution :
Rensselaer Polytech. Inst., Troy, NY, USA
fDate :
10/1/1995 12:00:00 AM
Abstract :
Online fault location in underground power distribution networks that involve interconnected lines (cables) and multiterminal sources continues to receive great attention, with limited success in techniques that would provide simple and practical solutions. This article features a new online fault location technique that: uses the pattern recognition feature of artificial neural networks (ANNs); and utilizes new capabilities of modern protective relaying hardware. The output of the neural network can be graphically displayed as a simple three-dimensional chart that can provide an operator with an instantaneous indication of the location of the fault
Keywords :
distribution networks; engineering graphics; fault location; neural nets; pattern recognition; power system analysis computing; underground distribution systems; artificial neural networks; computer simulation; graphical display; online fault location; pattern recognition; protective relaying hardware; three-dimensional chart; underground power distribution networks; Acoustic pulses; Artificial neural networks; Cables; Circuit breakers; Circuit faults; Fault location; Neural networks; Pattern recognition; Protection; Switches;
Journal_Title :
Computer Applications in Power, IEEE
