改进分段线性表示与动态时间弯曲相结合的负荷曲线聚类方法

聚类分析是负荷特性分类与综合的基本方法。针对现有聚类方法应用于基于电网大数据平台的在线负荷建模中在聚类质量、鲁棒性等方面表现的不足,提出一种改进分段线性表示(IPLR)的日负荷曲线降维方法。利用IPLR对数据组进行自适应降维重构的优点,与动态时间弯曲(DTW)距离适用于不等维时间序列间相似度衡量的特点相结合,构造出IPLR与DTW距离相结合的日负荷曲线聚类方法。首先,根据负荷曲线相邻及间隔采样点变化量,提取负荷曲线的特征点,对曲线进行自适应降维重构;然后,以DTW距离作为曲线相似度衡量指标,运用基于Canopy的K均值(CK-means)算法对降维数据组展开聚类分析。将所提出的方法应用于中国某省区电网典型用户日负荷曲线的分类与综合,结果表明所提降维方法与相似度衡量方法相契合,具有良好的综合性能,适用于变电站综合负荷行业构成比例解析。

Load Curve Clustering Method Combining Improved Piecewise Linear Representation and Dynamic Time Warping

Clustering analysis is the basic method for the load characteristic classification and synthesis. Aiming at the shortcomings in clustering quality and robustness of existing clustering methods applied to online load modeling based on the power grid big data platform, this paper proposes an improved piecewise linear representation (IPLR) method for daily load curve dimension reduction. Based on the advantages of IPLR for adaptive dimension reduction and reconstruction of data sets, combined with the characteristics of dynamic time warping (DTW) distance which is suitable for measuring the similarity between time series of unequal dimension, a daily load curve clustering method combining IPLR and DTW distance is constructed. Firstly, according to the variation of adjacent and interval sampling points of load curves, the characteristic points of load curves are extracted, and the curves are reconstructed by adaptive dimension reduction. Then, the DTW distance is taken as the similarity measurement index, and the clustering analysis of dimension reduction data is carried out by using Canopy based K-means (CK-means) algorithm. The method proposed is applied to the classification and synthesis of the daily load curves of typical consumers in a provincial power grid of China. The results show that the proposed dimension reduction method matches with the similarity measurement method, has good comprehensive performance, and is suitable for the analysis of the industrial composition ratio of synthesis load in substations.