自适应模糊C均值聚类算法的电力负荷特性分类技术研究-《郑州大学学报(工学版)》

文章信息/Info

Title:: Power Load Characteristic Classification Technology Research Based on An Improved Fuzzy C-means Clustering Algorithm

Author(s):: ZHAO Guosheng1; NIU Zhenzhen1; LIU Yongguang2; SUN Chaoliang2; ( 1.School of Electrical Engineering，Zhengzhou University，Zhengzhou 450001 , China;2.Henan Xu Ji Instrument Co.Ixd,Xuchang 461000，China)

摘要:: 针对传统模糊C均值聚类算法(FCM)存在的缺点,提出了一种自适应FCM算法,该算法以类内距离MIA和类间距离MDC两个聚类结果评价指标为基础,把MDC和MIA的比值1作为自适应函数来确定FCM算法的聚类数目c;同时,根据模糊决策的方法,利用FCM算法的目标函数和划分嫡来共同确定最优的模糊加权指数m的取值.结果表明:该算法不仅能够克服FCM算法无法自动确定聚类数目和模糊加权指数需要凭经验给出的缺点,而且得到的聚类结果是最优的,通过算例分析也证明了该算法的正确性和有效性.

Abstract:: In view of the disadvantages of the traditional Fuy C-means clustering algorithm， the author pro-poses an adaptive FCM algorithm. This algorithm is based on two clustering results evaluation index of withinthe class distance MIA and between the class distance MDC. The ratio of MDC and MIA，defined as l, is anadaptive function to determine the clustering number c of FCM algorithm. At the same time，according to thefuzy decision method，we use the objective function and partition entropy of FCM algorithm together to deter-mine the value of optimal fuzzy weighted m. ’This algorithm not only overcomes the FCM algorithm disadvan-tage of not being able to determine the clustering number automatically and fuzzy weighted index needs to begiven by experience,but also the clustering result is optimal. Finally,the correctness and effectiveness of thealgorithm were proved through example analysis.