Open AccessTopology Verification of Low Voltage Distribution Network Based on k-means Clustering Algorithm
Author(s) -
Junyi Wang,
Xingquan Ji,
Kejun Li,
Qi Sun
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/5/052096
Subject(s) - cluster analysis , algorithm , computer science , voltage , topology (electrical circuits) , network topology , transformer , selection (genetic algorithm) , similarity (geometry) , set (abstract data type) , data mining , mathematics , engineering , artificial intelligence , electrical engineering , combinatorics , image (mathematics) , programming language , operating system
Aiming at the problem of topology connection error existing in GIS system of low voltage distribution network, a topology verification algorithm using AMI voltage measurement data combined with k-means clustering algorithm is proposed. Firstly, the voltage data of the consumers in the low-voltage substation area is obtained by the AMI measurement system. Then k-means clustering algorithm is used by the similarity to cluster the voltage curves to identify and verify the users connected on the incorrectly transformer stations. An improved method of noise processing using data density set is proposed to solve the problem of initial cluster centre selection in k-means. For the problem of k value selection, an automatic correction of optimal k value is proposed. The feasibility and effectiveness of the improved k-means clustering algorithm in topology verification are verified by a practical example.