基于改进自适应遗传算法的系统可观测PMU最优配置  

Optimal Location of Phasor Measurement Unit for Complete Observability Based on Improved Adaptive Genetic Algorithm

作　　者：刘杰[1] 

机构地区：[1]广东省电力调度中心,广州510600

出　　处：《广东电力》2008年第12期13-17,共5页Guangdong Electric Power

摘　　要：以电力系统配置同步相量测量单元(PMU)个数最少、系统有最大测量冗余度为目标,全网可观测为约束,提出PMU最优配置模型,同时针对实际电网中存在某些重要节点已经初步安装PMU或者必须安装PMU的情况,提出了特殊约束条件,并给出了相应的求解算法。在此基础上,用改进自适应遗传算法求解此模型,保证全局最优。对某省49节点电网进行的计算表明,改进的自适应遗传算法收敛到全局最优解的概率优于传统的遗传算法和自适应遗传算法,更适用于工程实际。Taking the minimum number of synchronous phasor measurement unit （PMU）, the maximum state measurement redundancy and the full network observability of power system operation state as objective function, an optimal PMU placement （OPP） model is proposed. Furthermore, since PMUs have been or must be installed at several important nodes in actual power grid, this paper presents particular constraint conditions and gives the corresponding algorithm. On this basis, an improved adaptive genetic algorithm is presented to solve the model to ensure global optimum. The calculation on a practical 49-node power grid shows that the probability of improved adaptive genetic algorithm converging at the global optimum is better than that of the traditional genetic algorithm and adaptive genetic algorithm; therefore the improved method is more practical.

关 键 词：电力系统 相量测量单元 PMU配置 遗传算法 

分 类 号：TM734[电气工程—电力系统及自动化]