一种改进的遗传算法在无功优化中的应用  

Application of an Improved Genetic Algorithm in Reactive Power Optimization

作　　者：肖力[1] 

机构地区：[1]鄂州大学计算机系,湖北鄂州436000

出　　处：《计算机仿真》2010年第8期278-281,共4页Computer Simulation

摘　　要：针对电力系统无功优化问题,采用遗传算法(GA)是一种有效的全局优化概率搜索算法。为了降低系统有功损耗,保证无功电力在规定范围之内,将混沌搜索引入到标准遗传算法,提出了一种结合混沌搜索的改进遗传算法。通过混沌序列搜索产生初始种群,同时对交叉、变异、选择算子进行了改进,进一步改善了遗传算法的全局寻优能力,并有效防止了局部收敛,提高了算法的收敛速度和计算精度。对系统进行了无功优化,并进行仿真。仿真结果表明,改进的遗传算法能更精确地寻找到全局最优解,并明显加快了收敛速度,性能优于标准遗传算法。Genetic algorithm（GA） is an effective global optimization probability searching method Chaos searching is incorporated into the GA,and an improved genetic algorithm combining with chaos searching is proposed.Produced the initial population by the chaos sequence searching,and simultaneously improved the crossover operators,mutation operators and choice operator.The proposed algorithm not only has all the features of the improve genetic algorithm,but also further improves the global searching ability,local convergence and computational precision of the adaptive genetic algorithm.The optimization of IEEE 14,118 bus system is performed,The simulative results show that the proposed algorithm is superior to the genetic algorithm.

关 键 词：无功优化 遗传算法 混沌搜索 

分 类 号：TM712[电气工程—电力系统及自动化]