JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (4): 41-46.doi: 10.6040/j.issn.1672-3961.0.2015.421

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Application of improved K-means clustering algorithm based on density in distribution network block partitioning

JI Xingquan1, HAN Guozheng1*, LI Kejun2, FU Rongrong1, ZHU Yanghe1   

  1. 1.College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, Shandong, China;
    2. College of Electrical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2015-12-20 Online:2016-08-20 Published:2015-12-20

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Abstract: Based on the position, number and capacity of the electric substations in the urban medium voltage distribution network, an improved K-means clustering algorithm based on density was proposed. The two aspects in the selection of the initial cluster centers and the optimal cluster number K were improved. And the evaluation function based on intra-cluster variation and inter-cluster variation was proposed to evaluate the quality of clustering results. The distribution network was divided into some suitable distribution grids. The substations that were close in distance were divided into the same grid, and each grid was independent of power-supplying, which avoided the contact between the substations that were too far away and provided support for the optimization of the network structure in the distribution network. The results of calculated example showed the effectiveness of the proposed method.

Key words: K-means clustering algorithm, distribution network, electric substations, powersupplying block partition, evaluation function

CLC Number: 

  • TM715
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