改进型遗传算法在滤波器优化设计中的应用  

Application of improved genetic algorithm in optimal filter allocation

作　　者：宋微浪[1] 蔡金锭[1] 孙轶群[2] 江修波[1] 

机构地区：[1]福州大学电气工程与自动化学院,福建福州350108 [2]福建省电力有限公司,福建福州350002

出　　处：《电力自动化设备》2010年第6期83-86,共4页Electric Power Automation Equipment

摘　　要：提出一种引入神经网络函数的自适应遗传算法,并将其应用到电力系统滤波器优化配置,致力于解决电网谐波污染问题。根据工程实际情况,建立滤波器的简化模型,以滤波器投资最少为目标函数,以各节点谐波电压指标作为约束条件,采用改进的遗传算法进行寻优,最终实现有源电力滤波器的最优安装地点、安装个数和参数的优化选择。通过对IEEE实例计算与仿真分析,充分论证了所提出的改进算法及模型是正确而可行的。An adaptive genetic algorithm with neural network function is presented and applied in the optimal allocation of power system filters to suppress the harmonic pollution of power grid.A simplified model of filter is established according to the actual situation.With the minimum filter investment as the objective function and the node harmonic voltage index as the restriction,the improved genetic algorithm is applied to optimize the location and quantity of filter installation and the parameters of filter installed.The calculation and simulation of an IEEE case prove the improved algorithm and model are correct and feasible.

关 键 词：电力系统 改进遗传算法 有源电力滤波器 优化配置 神经网络函数 

分 类 号：TN713[电子电信—电路与系统] TP18[自动化与计算机技术—控制理论与控制工程]