Novel Adaptive Genetic Algorithm for Reactive Power Optimization of Power System

2009 ◽  
Author(s):  
Guang Yang ◽  
Xin-Rong Liu
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Reactive Power ◽  
Power Optimization ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization

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 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.

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