基于改进遗传算法的多目标无功优化

MULTIPLE-OBJECTIVE REACTIVE POWER OPTIMIZATION BASED ON IMPROVED GENETIC ALGORITHM

阐述了用于无功优化的改进遗传算法,在已有改进简单遗传算法的基础上,提出在含有多个目标的目标函数中采用线性变化和指数变化规律的越界罚系数,并对适应度函数进行模拟退火修正以保持种群的多样性和加快收敛;采用遗传因子自适应变化和改进的变异操作,可使遗传算法的全局优化和局部寻优能力大为提高。IEEE14节点系统的仿真计算结果表明,该方法在计算速度和收敛能力上优于简单遗传算法,且罚系数采用指数规律变化比采用定值或线性变化规律时收敛能力有明显改善。

A modified genetic algorithm for reactive power optimization is presented. On the basis of the existing improved genetic algorithm, it is pointed out that to ensure the population diversity and to speed up the convergence the penalty coefficient with linear and exponential variation laws is applied to the objective function containing multi-objects, and the sufficiency function is modified by simulated annealing. Using the adaptive variance of the genetic factor and the improved variation operation, the global and partial optimization ability can be obviously improved. The results of calculation and simulation for IEEE 14-bus system show that in the aspects of calculation speed and convergence ability the presented method is better than simple genetic algorithm, and the convergence speed will be faster when the penalty coefficient varies by exponential law rather than the constant or linear variation law.