摘要
在函数的全局优化算法中,模拟退火算法和遗传算法的结合可较好地改善算法的性能.基于这个思想将适合全局搜索的遗传算法(GA)和适合局部搜索的模拟退火算法(SA)相结合,提出改进的遗传模拟退火混合算法(IGASA)来解决电力系统PMU优化配置问题.该算法用于遗传算法中选择概率的计算以增强算法的收敛性,在交叉和变异概率的选取上也进行了改进,以进一步改善算法的稳定性和收敛性,并提高了收敛速度和防止种群早熟现象.5个仿真试验验证了该算法的可行性和有效性.
Among the global optimization algorithms of functions, hybrid SAGA can improve the performance of algorithms. Since the SA is suitable for global searching and the GA is suitable for local searching, we propose an ISAGA to solve the PMU placement of the power system. The selective probability, cross probability and mutation probability of the proposed algorithm are improved to enhance algorithm stability and convergence as well as its search efficiency and its capability to converge to good global optimum. Five simulation tests show that the IGASA is feasible and effective.
出处
《甘肃科学学报》
2008年第2期112-115,共4页
Journal of Gansu Sciences
关键词
电网
混合算法
PMU优化
可观性
power network,hybrid algorithm,PMU optimization,observability
