Application of catastrophic adaptive genetic algorithm to reactive power optimization in power system with wind farm

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.

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Reactive Power Optimization for Power System with Wind Farm Based on Improved Particle Swarm Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.668 ◽

2014 ◽

Vol 543-547 ◽

pp. 668-672

Author(s):

Han Ping Zhang

Keyword(s):

Power System ◽

Wind Farm ◽

Reactive Power ◽

Power Optimization ◽

Mathematical Problem ◽

Particle Swarm ◽

Particle Swarm Algorithm ◽

Reactive Power Optimization ◽

Multiple Variables ◽

Swarm Algorithm

The problem of power system reactive power optimization is a mathematical problem with multiple variables and constraints, which is complex and non-linear. The control variables include continuous variables and discrete variables; its difficult to get the optimal solution. A solution in power flow calculation is put forward after the mathematical model of wind farm is built in this paper. Based on this, taking the WSCC 9 nodes system as an example, use the particle swarm algorithm to solve the reactive power optimization problem. The result shows that this algorithm has an apparent positive effect on reducing system power loss and improving system voltages.

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Reactive power optimization in power system based on improved niche genetic algorithm

2010 International Conference On Computer Design and Applications ◽

10.1109/iccda.2010.5541277 ◽

2010 ◽

Cited By ~ 6

Author(s):

Xin Ma ◽

Rui-Lan Liu

Keyword(s):

Genetic Algorithm ◽

Power System ◽

Reactive Power ◽

Power Optimization ◽

Reactive Power Optimization

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Ant colony search algorithm for optimal reactive power optimization

Serbian Journal of Electrical Engineering ◽

10.2298/sjee0601077l ◽

2006 ◽

Vol 3 (1) ◽

pp. 77-88 ◽

Cited By ~ 10

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).

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