Assessment on Fault-tolerance Performance Using Neural Network Model Based on Ant Colony Optimization Algorithm for Fault Diagnosis in Distribution Systems of Electric Power Systems

2007 ◽  
Author(s):  
Zhisheng Zhang ◽  
Yaming Sun
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Power Systems ◽  
Electric Power ◽  
Neural Network Model ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Electric Power Systems ◽  
Ant Colony Optimization Algorithm

A class of hybrid intelligent system for fault diagnosis in electric power systems

1998 ◽  
Vol 23 (1-3) ◽  
pp. 207-224 ◽  
Author(s):  
Patricia R.S Jota ◽  
Syed M Islam ◽  
Tony Wu ◽  
Gerard Ledwich
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Electric Power ◽  
Intelligent System ◽  
Electric Power Systems ◽  
Hybrid Intelligent System

scholarly journals A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Agustín Flores ◽  
Eduardo Quiles ◽  
Emilio García ◽  
Francisco Morant ◽  
Antonio Correcher
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Power System ◽  
Transmission Line ◽  
Electric Power ◽  
Diagnostic System ◽  
Specific Area ◽  
Electric Power Systems ◽  
Electric Power System ◽  
Power Network

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

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