基于改进遗传算法的无功综合优化

简要分析了传统的电力系统无功优化方法的局限性之后，提出了一种快速有效的求解方法——改进的遗传算法(IGA)。在简单遗传算法(SGA)的基础上，提出了自适应遗传算法，该算法采取了与个体分布散度成正比，并随最优个体保留代数成指数上升的自适应变异率；同时也采取了自适应的交叉率．该交叉率与群体中最大的适应度值和每代群体的平均适应度值有密切的关系。算例表明提出的算法优化效果好．而且在精度上和收敛速度上都有较大的提高。

Reactive power optimization of integrative power system based on improved genetic algorithm

After the limitation of traditional reactive power optimization methods for power system has been briefly analyzed,an effective and fast method is put forward-the improved genetic algorithm(IGA). A self-adaptive genetic algorithm is put forward on the basis of simple genetic algorithm(SGA). It applies self-adaptive variation rate,which is proportional to individual distribution scatter degree and rises exponentially with the times of optimum individual reserve generation. It also applies self-adaptive crossing rate,which has close relation with the maximum colony adaptation degree and the average colony adaptation degree of each generation. The calculation example indicates that this algorithm has the optimum results with improved precision and convergence speed.