改进免疫遗传算法在DG系统无功优化中的应用

Application of Improved Immune Genetic Algorithm in DG Reactive Power Optimization

传统遗传算法(GA)自身存在早熟收敛、随机漫游和退化等难以解决的问题,使得该算法在含分布式发电的配电网无功优化应用中受到限制。将人工免疫系统中抗体多样性等特点融入到遗传算法中,形成改进免疫遗传算法(IGA)。采用保优抗体中的免疫疫苗,接种最佳基因的新方法,加快了计算速度,克服了早熟现象。将IGA应用于含分布式发电的IEEE-33节点和IEEE-57节点系统的无功优化计算,结果表明,IGA的收敛速度和目标函数均优于遗传算法,电压得到了改善。

The traditional genetic algorithm （GA） exists problems of the premature convergence, random roaming and degradation which is difficult to solve. These make the algorithm limited to the application of reactive power optimiza- tion of distributed power generation in power distribution network. In this paper, the diversity of antibodies of artificial immune system merges into the genetic algorithms, an improved immune genetic algorithm（IGA）is proposed, which is using the immune seeding of optimal antibody and inoculating the best genes to accelerate the calculation speed, and to overcome the precocious phenomena. IGA is applied to the reactive power optimization calculation of the distributed power generation IEEE-33 bus and IEEE-57 bus system. The results show that IGA convergence speed and the objec- tive function are better than the trational genetic algorithm, and the voltage has been improved.