DocumentCode :
721087
Title :
A Novel Visualization Method of Power Transmission Lines
Author :
Shuwei Pei ; Xiaofu Huang ; Bin Sheng ; Lizhuang Ma ; Dan Wu ; Ping Li
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2015
fDate :
20-22 April 2015
Firstpage :
358
Lastpage :
361
Abstract :
Visualization plays an important role in the analysis of grid circuit topological structure. The traditional line visualization method based on geographical position usually contains such problems that the nodes density distribution is uneven and the lines are too complex to understand. Some other methods completely neglect the real geographic location information. In this paper, we propose a new visualization method by adjustment of the nodes´ location and optimization of drawing lines. Initially, the scheduling substation node is designed as the central node, the other nodes are divides into different groups by k-means++ cluster algorithm. Next, the location of the nodes is adjusted and optimized after clustering, and the data density is balanced. Finally, the nodes are connected with the colored lines of ring or radial structure. Experimental results show that our visualization method can more clearly show the topological structure of the current power transmission lines and retain the main geographical location information.
Keywords :
graph theory; pattern clustering; power engineering computing; power generation scheduling; power grids; power transmission lines; central node; colored lines; data density; drawing line optimization; geographic location information; geographical position; grid circuit topological structure analysis; k-means++ cluster algorithm; node density distribution; node location; power transmission lines; radial structure; ring structure; scheduling substation node; visualization method; Algorithm design and analysis; Clustering algorithms; Data visualization; Image color analysis; Power transmission lines; Substations; Topology; -geographic; visualization; lines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Big Data (BigMM), 2015 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-8687-3
Type :
conf
DOI :
10.1109/BigMM.2015.11
Filename :
7153914
Link To Document :
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