Transmission Network Planing Using Genetic Algorithms

1999 ◽  
pp. 91-111
Author(s):  
M. R. Irving ◽  
H. M. Chebbo ◽  
S. O. Orero
Keyword(s):  
Genetic Algorithms ◽  
Transmission Network

Fault Diagnosis in Transmission Network Based on Optimization Technique

2012 ◽  
Vol 433-440 ◽  
pp. 3395-3399
Author(s):  
Hong Bo Cheng
Keyword(s):  
Genetic Algorithms ◽  
Fault Diagnosis ◽  
Power System ◽  
Network Topology ◽  
Optimization Technique ◽  
Transmission Network ◽  
Integer Programming Problem ◽  
Heuristic Knowledge ◽  
Scada System ◽  
Configuration Information

The fault diagnosis in power system is treated as a 0-1 integer programming problem use the switching and action information collected by SCADA system, combined with the analysis of protect. A comprehensive objective function has been established and genetic algorithms has been used to solve it. This approach has taken fully advantage of the characteristics of the power system's protection and the network topology configuration information. The optimized method is used to locate the fault as possible as fast. Rigorous theory of the method does not require the introduction of heuristic knowledge, and it can adapt to changes in network topology, and can used both in the single failure of power system and multiple failures in power system too.

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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