基于空间密度聚类和异常数据域的负荷异常值识别方法

针对海量电力负荷数据,提出一种基于空间密度聚类和异常数据域的负荷异常值识别方法.首先,基于空间密度聚类方法将负荷曲线按照正常和异常用电模式进行分类,并对正常用电模式中的负荷曲线进行负荷水平分类.然后,在不同负荷水平下,利用负荷期望值的置信区间和负荷样本与样本均值之间偏差的四分位差,构建异常数据域.考虑非典型用电行为的偶然性,引入用电时刻偏移量,对形成的异常数据域进行修正,并构建面向异常用电模式的异常数据域.在算例中,采用居民和工业用户的负荷数据集对所提方法进行检验,相比于传统方法,文中所提方法的识别精确率平均提高了10％以上,综合评价指标平均提高了4％以上.

Identification Method of Load Outlier Based on Density-based Spatial Clustering and Outlier Boundaries

For mass power load data, a method based on density-based spatial clustering and outlier boundaries is proposed to identify the load outlier. Firstly, the density-based spatial clustering method is used to classify load curves into normal and abnormal power consumption patterns. Also, the load curves with normal power consumption pattern are classified into different load levels. Then, the outlier boundaries are built using the confidence interval of load expected value and the inter-quartile range of the deviation between load sample and sample average at different load levels. Considering the contingencies of atypical power consumption behavior, the obtained outlier boundaries are corrected by time offset of power consumption, and outlier boundaries for abnormal power consumption patterns are built. Finally, the proposed method is tested in the example with the load data sets of residential and industrial users. Compared with the traditional method, the precision of the proposed method is improved over 10% on average, and the comprehensive evaluation index is improved over 4% on average.