基于改进遗传算法的无功优化

遗传算法的基础上对其局限性进行改进,使该算法在电力系统无功优化的应用中具有一定优越性。通过改进编码和选择算子,自适应的交叉变异概率等策略,并引入基于模拟退火策略的适应度函数和混沌算法,使得改进遗传算法高速、准确的收敛于最优解,改善了传统遗传算法易陷入收敛性差、效率低的弊端。在此基础上建立无功优化数学模型,介绍了该算法具体实现步骤,并将其应用于IEEE30节点,证明所提算法是可行和有效的。

Reactive Power Optimization based on Improved Hybrid Genetic Algorithm

On the basis of using genetic algorithm, this paper through improving the shortcomings of the theory to make the algorithm have some advantages in application to reactive power optimization in power system, The theory：improved encoded, selection operator, self-adaptively crossover rate and mutation rate, and also compounded the fitness function with simulated annealing and chaos algorithm, which made the improved genetic algorithm converge to the optimal solution with high speed as well as accurately. Traditional genetic algorithm＇ s shortcoming of bad convergency and efficiency is overcome. This paper established a mathematical model of reactive power optimization based on it and gives specific steps. The algorithm is tested on IEEE30-bus, and the results verify the feasibility and effectiveness of the proposed algorithm.