传统的遗传算法往往难以平衡搜索空间上的开发和探索能力，存在较大的随机性和盲目性，容易产生早熟收敛、局部搜索能力差和收敛速度缓慢等问题。自适应遗传算法的提出一定程度上改善了算法的性能，但也增大了算法陷入局部最优的可能。针对以上问题，本文研究的云自适应遗传算法是在传统的遗传算法的基础之上引入云理论，由X条件发生器自适应调整交叉变异概率，由于云模型云滴具有随机性和稳定倾向性，使交叉变异概率既具有传统自适应遗传算法（AGA）的趋势性，满足快速寻优，又具有随机性，当种群适应度最大时并非绝对的零值，有利于提高种群多样性，大大的改善了避免陷入局部最优的能力。本文无功优化的数学模型在考虑降低网损同时，综合考虑减少电压偏差和提高系统运行的电压稳定裕度。对标准IEEE30节点系统进行仿真计算，结果表明云自适应遗传算法具有很好的全局寻优能力和较快的收敛速度，能有效提高系统运行的经济性和安全性。%Traditional genetic algorithm to solve the complex problems that traditional genetic algorithm is often difficult to balance devel-opment and the ability to explore the search space, there is a greater randomness and blindness, prone to premature convergence and local search a-bility and convergence slow speed. Adaptive genetic algorithm proposed a certain extent, improve the performance of the algorithm, but also in-creases the algorithm into a local optimum possible. To solve the above problem, this paper cloud adaptive genetic algorithm is introduced in the foundation of the traditional genetic algorithm cloud theory, by the crossover and mutation probability the X condition generator adaptive adjust-ment, the randomness and stable tendency cloud model cloud droplets crossover and mutation probability of both a traditional AGA trend, the meet fast optimization, and a random, when a the populations fitness is not an absolute value of zero, help to improve the diversity of the population, greatly improving avoid fal ing into local the optimal capacity. Seeing minimum network loss as the objective function, the paper makes the simula-tion in standard IEEE14 node system. The results show that the CAGA algorithm can achieve a better optimal solution. In the paper, the model of multi- objective reactive power optimization includes net loss, voltage deviation and stable margin. The results of simulation in standard IEEE30 show that the CAGA algorithm possesses good global search ability and it converges quickly. Using the proposed method, the economy and security of power system operation can be effectively improved.
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