混合优化方法及其在电力系统无功优化中的应用

Hybrid optimization algorithm and its application for reactive power optimization of power systems

利用遗传算法和传统优化方法的互补特性,采用混合优化方法求解包含离散变量和连续变量的无功优化问题。遗传算法的选择、交叉和变异操作仅作用于离散变量,遗传算法对种群进行全局广度搜索,运用传统优化方法对种群个体中的连续变量进行优化使其移动到局部最优点上。为保证对连续变量的优化效果,选择了基于函数变换与广义逆的优化新算法。混合优化算法将遗传算法擅长处理离散变量和传统优化方法速度快、数值稳定性好的优势有机结合,模型简单、规范。算法的实用性和有效性通过算例及工程应用得到验证。

Hybrid optimization methods are used to resolve reactive power optimization with discrete and continuous variables, by which the complementary characteristics of genetic and traditional optimization algorithms are utilized. Genetic operations such as selection and crossover and mutation are acted on discrete variables only. The genetic algorithms are used to make the global searches to the population. Before a new offspring to select into the population it must to move to the local optimal point by using the traditional optimization methods to the continuous variables. To ensure the effects of the local optimization, a new optimization algorithm, which is based on function transform and generalized inverse of matrices, is used. The model of hybrid optimization algorithm is simplified and normal, which has the advantage of genetic algorithms treating discrete variables conveniently and the traditional optimization methods calculating fast and steady. The practicability and effectiveness of the algorithms are proved by case studies.