Genetic algorithm based optimization for allocation of static VAr compensators

2006 ◽  
Author(s):  
A.A. Alabduljabbar
Keyword(s):  
Genetic Algorithm ◽  
Static Var Compensators

scholarly journals Coordinated Tuning of a Group of Static Var Compensators Using Multi-Objective Genetic Algorithm

2011 ◽  
Vol 14 (1) ◽  
Author(s):  
Enrique Ramón Chaparro Viveros ◽  
Manuel Leonardo Sosa Ríos
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Reactive Power ◽  
Load Flow ◽  
Operating Conditions ◽  
Financial Investment ◽  
Multi Objective ◽  
Tuning Method ◽  
Static Var Compensators ◽  
Voltage Deviation

The optimal coordinated tuning of a group of Static Var Compensators (SVC), in steady state, allows the Power Electric Systems (PES) to operate close to their overload limits, maintaining the voltage stability in several operating conditions. The mentioned tuning problem was considered as a Multi- objective Optimization Problem (MOP) with three objectives to optimize: the financial investment for acquiring the set of compensators, the maximum voltage deviation and total active power loss. The Genetic Algorithm (GA), which belongs to the group of Evolutionary Algorithms, was utilized and adapted for MOP, obtaining a Multi-Objective GA (MOGA). The parameters to be adjusted in each compensator are: the reference voltage and the minimum and maximum reactive power injected to the system. In this work, the number of compensators and their locations were calculated using the Q-V sensitivity curve, from the Load Flow algorithm, based on Newton–Raphson method. The proposed coordinated tuning method will be validated considering an example of PES, where was located and tuned a specific set of compensators. Time simulations were made for dynamic performing the steady state coordinated tuning.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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