Pseudo-parallel genetic algorithm for reactive power optimization

2004 ◽  
Author(s):  
Z.H. Wang ◽  
X.G. Yin ◽  
Z. Zhang ◽  
J.C. Yang
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Reactive Power Optimization ◽  
Parallel Genetic Algorithm ◽  
Pseudo Parallel Genetic Algorithm

Power System Reactive Power Optimization Based on Improved Genetic Agorithm

2012 ◽  
Vol 614-615 ◽  
pp. 1361-1366
Author(s):  
Ai Ning Su ◽  
Hui Qiong Deng ◽  
Tian Wei Xing
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Fitness Function ◽  
Reactive Power Optimization ◽  
Penalty Factor ◽  
Mutation Strategy ◽  
Optimization Search

Reactive power optimization is an effective method for improving the electricity quality and reducing the power loss in power system, and it is a mixed nonlinear optimization problem, so the optimization process becomes very complicated. Genetic algorithm is a kind of adaptive global optimization search algorithm based on simulating biological genetic in the natural environment and evolutionary processes, can be used to solve complex optimization problems such as reactive power optimization. Genetic algorithm is used to solve reactive power optimization problem in this study, improved the basic genetic algorithm, included the select, crossover and mutation strategy, and proposed a individual fitness function with penalty factor. The proposed algorithm is applied to the IEEE9-bus system to calculate reactive power. The results show the superiority of the proposed model and algorithm.

Application of Tabu-Genetic Algorithm in Reactive Power Optimization

2014 ◽  
Vol 1006-1007 ◽  
pp. 1021-1025
Author(s):  
Song Tao Zhang ◽  
Gong Bao Wang ◽  
Hui Bo Wang
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Local Search ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Reactive Power Optimization ◽  
Simple Genetic Algorithm ◽  
Better Than

By using tabu search algorithm which has strong local search ability as mutation operator of genetic algorithm, the tabu-genetic algorithm is designed for reactive power optimization in this paper, the strong global search ability of genetic algorithm and strong local search ability of tabu search algorithm is combined, the disadvantage of weak local search ability of genetic algorithm is conquered. Otherwise, the over limit of population is recorded and filtered, to ensure the final individual is under limit and effective. The tabu-genetic algorithm and simple genetic algorithm are used for simulation of IEEE 14-bus system 500 times, the results indicate that the performance of the tabu-genetic algorithm is much better than the simple genetic algorithm, its local search ability is improved obviously, and the active power loss is reduced more.

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