基于分布式聚类模型的电力负荷特性分析
摘要：电力系统的负荷模型是决定电力系统可靠性的关键要素,传统的负荷特性数据聚类算法计算复杂、运行时间长。将K-means和Canopy聚类算法有机地结合,建立一种分布式聚类模型。在此基础上,对用户整点负荷数据进行归一化处理,利用负荷规范化区间值与24个整点时间的参数关系,得到聚类中心分布。以福建省历史日负荷数据为例,验证分布式聚类模型运行的快速性。结果表明:距离阈值T2与算法运行时间成反比;簇个数越多,运行时间越长;大工业行业聚类中心分布稳定,显著性不明显,农业生产行业聚类中心分布显著性明显,为预测用户负荷特征及用电特性提供思路借鉴。
Abstract:The load model of electric power system is a key factor to determine the reliability of power system, and conventional clustering algorithm of load characteristic data suffers such disadvantages as high computing complexity and long operation time. Therefore, a novel distributed clustering model is built by organically combining K-means and Canopy clustering algorithms. Based on this, users integral point load data are applied with normalization processing, the clustering center distribution can be obtained by using the relationship between load normalized interval values and parameters at 24 integral time points. Taking historical day load data of Fujian province as example, the operation rapidity of distribution cluster model is verified. Results show that distance threshold T2 is inversely proportional to the operation time, the more the number of clusters is the longer the operation time will be. In addition, the clustering center distribution of large-scale industry is stable, its significance is not obvious, while the significance of clustering center distribution of agricultural industry is obvious, which provide ideal references for predicting users load characteristics and power consumption features.
标题：基于分布式聚类模型的电力负荷特性分析
英文标题：Analysis of Power Load Characteristics Based on Distributed Clustering Model
作者：曾楠, 许元斌, 罗义旺, 刘青, 刘燕秋, 张欢,
关键词：电力系统, 负荷特性, 负荷建模, 聚类分析, 分类算法,
发表日期：2018-02-19
- 文件大小:
- 2.57 MB
- 下载次数:
- 60
-
高速下载
|
|
