混合遗传算法的电力系统无功优化

针对遗传算法（GA）的局限性，提出了一种应用于电力系统无功优化问题的混合遗传算法（GASA）。实施了最优保留策略，改进交叉和变异操作，并结合模拟退火算法（SA）的Metropolis判别准则的复制策略，使寻优过程能够跳出局部最优解，从而形成了混合遗传算法。优化过程中考虑了电力系统无功优化自身特点，提高了计算效率。对IEEE30节点系统的仿真表明：该算法能够有效地提高收敛速度，避免早熟收敛。

Reactive Power Optimization of Power System Using Hybrid Genetic Algorithm

In this paper a new hybrid genetic algorithm(GASA)for reactive power optimization is presented. Aiming at the limitation of genetic algorithm(GA), the optimized reserved strategy is used, and the cross and mutation method are improved. The hybrid genetic algorithm is formed combined with the Metropolis discrimination criteria of simulated annealing algorithm(SA), which can jump from local optimization solution. During the optimization, the self-features of reactive power optimization are considered, which will enhance the computational efficiency. Using the presented algorithm the reactive power optimization calculation for IEEE30-bus system is conducted, the results show that it can improve the speed of convergence, and avoid premature convergence.