带遗传算子模拟植物生长算法在AGC机组调配经济性中的应用  

Application of plant growth simulation algorithm with genetic operators in the economical AGC unit dispatch

作　　者：黄伟峰[1] 姚建刚[1] 韦亦龙 刘苏[1] 汤成艳[1] 

机构地区：[1]湖南大学电气与信息工程学院,湖南长沙410082

出　　处：《电力系统保护与控制》2015年第6期72-77,共6页Power System Protection and Control

摘　　要：建立AGC机组调配调节费用的数学模型,采用一种带遗传算子模拟植物生长算法(Plant Growth Simulation Algorithm,PGSA),应用于机组调配经济性研究。为解决完整模拟植物生长过程全局搜索可能速度过慢的问题,采用最优保留和最差杂交后保留的策略,可在加速计算速度的同时抑制早熟,防止陷入局部最优。此外,为防止新生长点过于聚集,采用K-means聚类算法对机组进行分类,并在长出新生长点时,同类机组选择差异性较大的变异方式。对于存在多个最优解,采用多解平均法,确保机组调配的公平性。通过计算结果对比验证了该算法的合理性及有效性。This paper establishes a mathematic model about the cost of dispatch and regulation of AGC units, uses a plant growth simulation algorithm with genetic operators, and applies the algorithm to the research of economical unit dispatch. In order to tackle the problem of low speed in full-scale search while having full simulation of plant growth, it adopts the method of reserving the best and reserving the better after the hybridization of the worst, which helps to accelerate the calculation speed while restraining precocity and avoiding from local optimum. In addition, to prevent excessive aggregation of growing points, it applies K-means clustering algorithm to classify for the units, and then chooses the variation of bigger otherness for the units of the same kind when growing new growing points. As for the multiple optimal results, it distributes equally for the unit to ensure the equality of units dispatch. By comparing the calculation results, the rationality and validity of this algorithm are verified.

关 键 词：遗传算子 模拟植物生长算法 K-MEANS聚类算法 AGC机组 调配经济性 

分 类 号：TM62[电气工程—电力系统及自动化]