论文标题
使用傅立叶系列压缩和统计聚类算法的智能表的相位识别
Phase Identification of Smart Meters Using a Fourier Series Compression and a Statistical Clustering Algorithm
论文作者
论文摘要
电气分配系统中相位连接性的准确标记对于维护和操作很重要，但通常是错误的或缺失的。在本文中，我们提出了一个过程，以在电压时间序列数据上使用分层聚类方法确定哪些智能计必须处于同一阶段。我们没有直接使用时间序列数据，而是应用傅立叶变换来表示数据域中的数据，而是删除$ 98 \％$ $的傅立叶系数，并使用其余系数将仪表群集成在同一阶段。通过确认仪表的群集（阶段）成员的仪表不会在两个月内发生变化，从而验证了这一过程的结果。此外，我们还确认分布网络中属于同一馈线的仪表正确分类为同一群集，即分配给同一阶段。
Accurate labeling of phase connectivity in electrical distribution systems is important for maintenance and operations but is often erroneous or missing. In this paper, we present a process to identify which smart meters must be in the same phase using a hierarchical clustering method on voltage time series data. Instead of working with the time series data directly, we apply the Fourier transform to represent the data in their frequency domain, remove $98\%$ of the Fourier coefficients, and use the remaining coefficients to cluster the meters are in the same phase. Result of this process is validated by confirming that cluster (phase) membership of meters does not change over two monthly periods. In addition, we also confirm that meters that belong to the same feeder within the distribution network are correctly classified into the same cluster, that is, assigned to the same phase.
