摘要
变尺度混沌优化(MSCOA)是一种改进的混沌优化方法(COA),利用混沌运动的内在随机性、遍历性和规律性进行全局寻优;通过尺度变换不断缩小优化变量的搜索空间,通过改变“二次搜索”的调节系数提高搜索精度,从而提高局部细化搜索能力。文章将该算法应用于电力系统无功优化问题,并对IEEE6节点系统以及某地区54节点系统进行了仿真计算,计算结果验证了算法的有效性。
Mutative scale chaos optimization algorithm (MSCOA) is a modified chaos optimization algorithm (COA), which possesses the properties of randomness, ergodicity and regularity of chaos movement. These intrinsic characteristics are employed by MSCOA to escape from the local minimum and implement global optimization. In MSCOA, the search space of optimized variable can be reduced continually by means of scale transform, and the search precision can be improved by changing the adjustment coefficient of quadratic search. Therefore, the refined local search ability is enhanced here. MSCOA is applied to reactive power optimization of power system and its effectiveness is validated by the simulation results of IEEE 6-bus system and a certain actual 54-bus system.
出处
《电网技术》
EI
CSCD
北大核心
2005年第11期20-24,29,共6页
Power System Technology
