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.

scholarly journals Ant colony search algorithm for optimal reactive power optimization

2006 ◽  
Vol 3 (1) ◽  
pp. 77-88 ◽  
Author(s):  
K. Lenin ◽  
M.R. Mohan
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Ant Colony ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Ant Colonies

The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents? approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical) test bus system. The proposed approach is tested and compared to genetic algorithm (GA), Adaptive Genetic Algorithm (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.

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