基于特性指标降维的日负荷曲线聚类分析  

Cluster Analysis of Daily Load Curves Using Load Pattern Indexes to Reduce Dimensions

作　　者：刘思[1] 李林芝[1] 吴浩[1] 孙维真 傅旭华 叶承晋 黄民翔[1] 

机构地区：[1]浙江大学电气工程学院,浙江省杭州市310027 [2]浙江电力调度控制中心,浙江省杭州市310007 [3]浙江省电力公司经济技术研究院,浙江省杭州市310008

出　　处：《电网技术》2016年第3期797-803,共7页Power System Technology

基　　金：国家电网公司科技项目(面向城乡差异化的空间负荷预测及统一应用模型研究;No.5211JY150016)~~

摘　　要：运用数据挖掘中的聚类技术对电力系统日负荷曲线进行分析,提出一种基于特性指标降维的日负荷曲线聚类方法——特性指标聚类(pattern index clustering,PIC),通过负荷率、日峰谷差率等6个日负荷特性指标对日负荷曲线进行降维处理,利用基于聚类有效性修正的德尔菲方法配置各指标权重,以加权欧式距离作为相似性判据,对日负荷曲线进行聚类。算例结果表明所提方法运行时间短,鲁棒性好,提高了负荷曲线聚类质量,能直观反映典型负荷曲线的特点。A new method, Pattern Index Clustering（PIC）, originated from clustering techniques in data mining, is proposed for daily load curve clustering in this paper. PIC method reduces dimensions of sequential load curves with six load pattern indexes, such as load factor and daily peak-valley ratio. Weights of the indexes are gradually corrected with clustering-validity-based Delphi method. Hence, the method clusters curves with weighted Euclidean distance as similarity measurement, resulting in a performance better than classic clustering algorithms. Numerical examples show that PIC method has shorter run time, stronger robustness and better clustered load curves. Therefore it reflects characteristics of typical load curves more directly.

关 键 词：负荷曲线分类 负荷特性指标 降维 加权欧式距离 聚类有效性 

分 类 号：TM721[电气工程—电力系统及自动化]