基于改进遗传模拟退火算法的PMU优化配置

PMU optimization configuration based on improved genetic simulated annealing algorithm

分析了遗传算法和模拟退火算法的优缺点,将具有较好全局寻优性能的遗传算法和具有较强局部搜索能力的模拟退火算法结合,形成的遗传模拟退火MGASA算法用于解决以电力系统状态完全可观测和PMU配置数目最小为目标的PMU优化配置问题。在寻优过程中,先将每一代群体进行遗传操作,再对产生的新群体中各个体进行模拟退火操作,同时在选择、交叉、变异和复制操作过程中实施最优保留策略,复制策略采用M etropolis判别准则。通过采用IEEE14和IEEE39节点系统对该算法进行验证表明,MGASA算法在解决PMU优化配置问题上具有较高的寻优性能和搜索效率。

The merits and demerits of the genetic algorithm （GA） and the simulated annealing （SA） algorithm are analyzed, and the improved genetic simulated annealing （MGASA）algorithm, which combines GA with good global optimization performance and SA with strong local optimization ability, was used to optimize PMU configuration with the purpose of complete observability of power system situation and minimum PMU amounts. In MGASA, the genetic operation was conducted for every generation colony first and then the simulated annealing operation was carried out for each individual of the newly-generated colony. Meanwhile, the optimal saving strategy was implemented during the process of selection, crossover, mutation and replication operations, and the replication strategy adopted Metropolis criteria. The IEEE 14-bus and IEEE 39-bus systems were used to examine MGASA and results show that it has good. optimization performance and efficiency in PMU configuration.