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 Modified Artificial Fish Swarm Algorithm in Power System Reactive Power Optimization

2013 ◽  
Vol 321-324 ◽  
pp. 1361-1364
Author(s):  
Shu Kui Liu ◽  
Na Dong ◽  
Zhi Zheng ◽  
Li Cheng ◽  
Qi Li
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Reactive Power Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Comparison Results ◽  
Artificial Fish Swarm ◽  
Network Losses ◽  
Swarm Algorithm

Modified Artificial Fish Swarm Algorithm (MAFSA) based on the global search characteristic of Artificial Fish Swarm Algorithm (AFSA), and combined with the local search of chao optimization algorithm(COA), can avoid trapping into local minimal value and decrease the iteration numbers, which was a swarm intelligence optimization algorithm applied to continuous space. MAFSA was proposed to optimize the reactive power optimization, which applied for optimal reactive power is evaluated on an IEEE 30-bus power system. The modeling of reactive power optimization is established taking the minimum network losses as the objective. The simulation results and the comparison results with various optimization algorithms demonstrated that the MAFSA converges to better solutions than other approaches and the algorithm can make effectively use in reactive power optimization. Simultaneously, the validity and superiority of MAFSA was proved.

Sign in / Sign up

Export Citation Format

Share Document