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Journal of Sensors
Volume 2015, Article ID 678120, 19 pages
http://dx.doi.org/10.1155/2015/678120
Research Article

Fault Line Selection Method of Small Current to Ground System Based on Atomic Sparse Decomposition and Extreme Learning Machine

1School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China
2School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China

Received 13 October 2014; Accepted 28 November 2014

Academic Editor: Sergiu Dan Stan

Copyright © 2015 Xiaowei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposed a fault line voting selection method based on atomic sparse decomposition (ASD) and extreme learning machine (ELM). Firstly, it adopted ASD algorithm to decompose zero sequence current of every feeder line at first two cycles and selected the first four atoms to construct main component atom library, fundamental atom library, and transient characteristic atom libraries 1 and 2, respectively. And it used information entropy theory to calculate the atom libraries; the measure values of information entropy are got. It constructed four ELM networks to train and test atom sample and then obtained every network accuracy. At last, it combined the ELM network output and confidence degree to vote and then compared the vote number to achieve fault line selection (FLS). Simulation experiment illustrated that the method accuracy is 100%, it is not affected by fault distance and transition resistance, and it has strong ability of antinoise interference.