Application of Improved Immune Genetic Algorithm to Reactive Power Optimization

Reactive power optimization is a main measure of power grid optimization and its nature is a multi-object and nonlinear mixed optimization problem.This paper adopts immune genetic algorithm,IGA,to study the solving process of this problem.Based on the traditional genetic algorithm,the immune genetic algorithm draws lessons from biotic diversity-maintenance and memory in antibody of immune mechanism,and improves the overall and local searching ability.The experiment shows that the IGA has good overall astringency and can effectively solve the reactive power optimization problem.