结合负荷形态指标的电力负荷曲线两步聚类算法  

A Two-Step Clustering Algorithm Combined with Load Shape Index for Power Load Curve

作　　者：彭勃[1] 张逸[2] 熊军 董树锋[1] 李永杰[1] 

机构地区：[1]浙江大学电气工程学院,杭州市310027 [2]国网福建省电力有限公司电力科学研究院,福州市350007 [3]国网厦门供电公司,福建省厦门市361000

出　　处：《电力建设》2016年第6期96-102,共7页Electric Power Construction

基　　金：国家高技术研究发展计划项目(863计划)(2014AA051901)~~

摘　　要：为改善基于欧式距离的全维度负荷曲线聚类算法在负荷形态相似度上的不足,提出了结合负荷形态特征指标的电力系统负荷曲线两步聚类算法。算法第一步采用基于欧式距离的负荷曲线聚类方法获得初步聚类结果,并通过负荷聚类评价指标选取一次聚类算法和聚类数目;第二步基于负荷形态特征指标采用监督学习算法对负荷进行重新分类。之后比较了不同算法的分类效果,最后给出了聚类结果的应用建议。算例结果表明,所提出的两步聚类算法可以改善传统的负荷曲线聚类方法在形态相似度上的不足,在二次分类方法中,支持向量机(support vector machine,SVM)算法表现较好,所提出的方法具有实际应用意义。To make up for the draw back that clustering method based on Euclidean Distance considering all dimensions of load curves is weak in load shape similarity,this paper proposes a two-step clustering algorithm combined with load shape characteristic index for power load curve. First,this algorithm obtains the preliminary clustering result by using clustering method based on the Euclidean Distance of load curves and selects the clustering method and number through cluster evaluation index. Second, this algorithm uses supervised learning algorithm to reclassify load based on load shape characteristic index. Then,different clustering algorithms are compared. At last,we propose the suggestions for the application of the clustering results. The example results show that the proposed two-step clustering algorithm can make up for the weakness of traditional clustering algorithm in load shape similarity. Support vector machine（ SVM） algorithm has better performance in the second-step classification. The proposed algorithm has practical significance.

关 键 词：负荷聚类 电力数据挖掘 负荷形态 监督学习算法 

分 类 号：TM714[电气工程—电力系统及自动化]