•  
  •  
 

Keywords

fast derivative dynamic time-regularization algorithm; K-median clustering; station area topologyidentification; household-transformer relationship; phase relationship

Abstract

To study the hierarchical relationship of branch nodes involving the identification of distributed power generation access and effectively perform the topology identification of distribution station areas, a topology identification method combining the fast derivative dynamic time-regularization algorithm (FDDTW) and K-median clustering is proposed for low-voltage active distribution station areas. Firstly, under the premise of not changing the data characteristics, the input original voltage data of the station area are normalized by utilizing the characteristics of removing the mean and standardizing the variance of the Z-score standardization method. Secondly, the distance value is calculated by adopting the FDDTW algorithm; the clustering number is set according to the number of distribution transformers divided in the station area, and the household-transformer relationship is clustered by utilizing K-median clustering. Thirdly, based on the clustering result of the household-transformer relationship, the distance value inside the station area is calculated again by using the FDDTW algorithm, and the phase relationship is clustered. Finally, the method is compared and analyzed by adopting experimental simulation. The research results show that the calculation time of the FDDTW algorithm is shortened by 28.6% compared with that of DTW, and the accuracy rate of the FDDTW algorithm is improved by 23.8 percentage points compared with that of DTW.

DOI

10.19781/j.issn.1673-9140.2026.02.010

First Page

107

Last Page

116

Download

Share

COinS