密度梯度聚类算法在负荷动特性聚类中的应用

Application of Data Mining Based on Density Gradient in Load Dynamic Characteristic Aggregate

文中提出将基于数据挖掘原理的密度梯度聚类算法应用于电力系统负荷动特性的聚类。通过计算实测响应空间各样本间的密度分布,得到原始聚类中心,再利用类与类之间边界点的分布情况进行合并,从而达到负荷动特性分类。聚类中心在分类过程中产生,以此聚类中心作为该类的等效样本,对其进行参数辨识即可得到同类负荷特性的通用负荷模型。分类和综合在同一过程中完成,具有快速简便的优点。对某一变电站现场采集的负荷特性数据进行聚类应用,结果表明该方法具有良好的聚类效果。

This article presents a density gradient clustering method based on data mining for load dynamic characteristics aggregate. By calculating the density of measured response space data sample and its neighbors, the original centers of clusters is obtained;then combination is completed according to the distribution of border points between clusters. Clustering center appears in the classifying process,which is taken as equivalent sample,and by identifying the equivalent sample, the synthetic load model of this classification is obtained. The classification and synthesis are completed in the same process so the method is convenient. By synthesizing the dynamic load characteristics data collected from a substation,the correctness and effectiveness of the method is proved.