Assessment of Genetic Algorithm selection, crossover and mutation techniques in reactive power optimization

2009 ◽  
Author(s):  
Muhammad Tami Al-Hajri ◽  
M. A. Abido
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Reactive Power Optimization ◽  
Algorithm Selection ◽  
Crossover And Mutation

Reactive Power Optimization Based on CAGA Algorithm

2012 ◽  
Vol 616-618 ◽  
pp. 2210-2213
Author(s):  
Li Jun Chen ◽  
Ran Ran Hai ◽  
Ya Hong Zhang ◽  
Gang Gang Xu
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Cloud Model ◽  
Optimal Solution ◽  
Population Diversity ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Global Optimal ◽  
Crossover And Mutation

Reactive power optimization is a typical high-dimensional, nonlinear, discontinuous problem. Traditional Genetic algorithm(GA) exists precocious phenomenon and is easy to be trapped in local minima. To overcome this shortcoming, this article will introduce cloud model into Adaptive Genetic Algorithm (AGA), adaptively adjust crossover and mutation probability according to the X-condition cloud generator to use the randomness and stable tendency of droplets in cloud model. The article proposes the cloud adaptive genetic algorithm(CAGA) ,according to the theory, which probability values have both stability and randomness, so, the algorithm have both rapidity and population diversity. Considering minimum network loss as the objective function, make the simulation in standard IEEE 14 node system. The results show that the improved CAGA can achieve a better global optimal solution compared with GA and AGA.

Reactive Power Optimization of Hydropower Station Based on Improved Genetic Algorithm

2011 ◽  
Vol 71-78 ◽  
pp. 2214-2217
Author(s):  
Wei Tian ◽  
Jin Chan Wang ◽  
Yu Cheng Ye ◽  
Wei Chen
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Hydropower Station ◽  
Reactive Power Optimization ◽  
Improved Genetic Algorithm ◽  
C Language ◽  
Crossover Probability ◽  
Mutation Strategy ◽  
Crossover And Mutation

In this paper, an improved GA was proposed to minimum the disadvantages of classic GA. This modified GA improved the crossover and mutation strategy by using of sort program to arrange chromosome from big to small. The crossover probability and mutation probability were decided by the numbers of the order. Samples were chosen to take part in interbreeding. The convergence speed and results can be improved in this way. Moreover, premature convergence and local convergence were avoided at the same time. The modified GA was implemented by an optimization program compiled in visual C++ language. It has been successfully applied in the reactive power optimization of a Hydropower station. The results showed the feasibility and validity of this modified 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.

Sign in / Sign up

Export Citation Format

Share Document