基于改进遗传算法和内点法的无功优化混合算法

Hybrid method for ORPF based on improved GA and IPM

文章针对具有离散变量和连续变量共存的高维大规模无功优化问题,采用非线性内点法和改进遗传算法交替求解的混合算法,在迭代的不同阶段,分别对内点法和改进遗传算法进行收敛条件改进,使二者的优化结果互为基础、相互利用,保证了混合算法的整体寻优效率。IEEE118节点系统的无功优化计算表明,所提混合算法可有效提高单一算法的收敛性能和运算速度。

The high dimensional and large scale reactive power optimization problem with continuous variables and discrete variables is solved by the hybrid method in which the nonlinear interior point method（IPM） and the improved genetic algorithm（GA） are used alternately. In different iteration phases, the convergence conditions of the IPM and GA are dynamically adjusted,and the IPM and GA take advantage of each other, so that the efficiency of the hybrid method is greatly improved. Numeri- cal simulations on the IEEE 118 test system illustrate that the proposed hybrid method is effective in both convergence performance and calculation speed.