A hybrid genetic algorithm for optimal reactive power planning based upon successive linear programming

1999 ◽  
Vol 14 (4) ◽  
pp. 1292-1298 ◽  
Author(s):  
A.J. Urdaneta ◽  
J.F. Gomez ◽  
E. Sorrentino ◽  
L. Flores ◽  
R. Diaz
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Reactive Power ◽  
Hybrid Genetic Algorithm ◽  
Successive Linear Programming ◽  
Reactive Power Planning

A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem

2012 ◽  
pp. 56-71
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfill the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

scholarly journals The Optimization of the Location and Capacity of Reactive Power Generation Units, Using a Hybrid Genetic Algorithm Incorporated by the Bus Impedance Power-Flow Calculation Method

2020 ◽  
Vol 10 (3) ◽  
pp. 1034
Author(s):  
Insu Kim
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Calculation Method ◽  
Power Flow ◽  
Distribution Systems ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Hybrid Genetic Algorithm ◽  
Power Flow Calculation ◽  
Flow Calculation

Dynamic and static reactive power resources have become an important means of maintaining the stability and reliability of power system networks. For example, if reactive power is not appropriately compensated for in transmission and distribution systems, the receiving end voltage may fall dramatically, or the load voltage may increase to a level that trips protection devices. However, none of the previous optimal power-flow studies for reactive power generation (RPG) units have optimized the location and capacity of RPG units by the bus impedance matrix power-flow calculation method. Thus, this study proposes a genetic algorithm that optimizes the location and capacity of RPG units, which is implemented by MATLAB. In addition, this study enhances the algorithm by incorporating bus impedance power-flow calculation method into the algorithm. The proposed hybrid algorithm is shown to be valid when applied to well-known IEEE test systems.

Towards a Hybrid Solver: Integration of a Genetic Algorithm Within “DSIDES”

2002 ◽  
Author(s):  
Shuiwei Xie ◽  
Warren F. Smith
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Decision Support ◽  
Hybrid Genetic Algorithm ◽  
Systems Design ◽  
Nonlinear Problems ◽  
The Body ◽  
Hybrid Solver ◽  
Decision Based Design

In contributing to the body of knowledge for decision-based design, the work reported in this paper has involved steps towards building a hybrid genetic algorithm to address systems design. Highlighted is a work in progress at the Australian Defence Force Academy (ADFA). A genetic algorithm (GA) is proposed to deal with discrete aspects of a design model (e.g., allocation of space to function) and a sequential linear programming (SLP) method for the continuous aspects (e.g., sizing). Our historical Decision Based Design (DBD) tool has been the code DSIDES (Decision Support In the Design of Engineering Systems). The original functionality of DSIDES was to solve linear and non-linear goal programming styled problems using linear programming (LP) and sequential (adaptive) linear programming (SLP/ALP). We seek to enhance DSIDES’s solver capability by the addition of genetic algorithms. We will also develop the appropriate tools to deal with the decomposition and synthesis implied. The foundational paradigm for DSIDES, which remains unchanged, is the Decision Support Problem Technique (DSPT). Through introducing genetic algorithms as solvers in DSIDES, the intention is to improve the likelihood of finding the global minimum (for the formulated model) as well as the ability of dealing more effectively with nonlinear problems which have discrete variables, undifferentiable objective functions or undifferentiable constraints. Using some numerical examples and a practical ship design case study, the proposed GA based method is demonstrated to be better in maintaining diversity of populations, preventing premature convergence, compared with other similar GAs. It also has similar effectiveness in finding the solutions as the original ALP DSIDES solver.

OPTIMAL REACTIVE POWER PLANNING USING GENETIC ALGORITHM

2002 ◽  
Vol 25 (2) ◽  
pp. 31-42
Author(s):  
G. E. M. Aly ◽  
a. El-Desouki ◽  
Amany El-Zonkoly
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Reactive Power Planning
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