Fault Line Selection Method of Small Current to Ground System Based on Atomic Sparse Decomposition and Extreme Learning Machine
| Autor: | Xiangxiang Wei, Zeng Zhihui, Hou Yaxiao, Jie Gao, Wei Yanfang, Xiaowei Wang |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Engineering
Article Subject Degree (graph theory) business.industry Sparse approximation Interference (wave propagation) Symmetrical components Measure (mathematics) Control and Systems Engineering lcsh:Technology (General) lcsh:T1-995 Electrical and Electronic Engineering Feeder line business Instrumentation Algorithm Selection (genetic algorithm) Extreme learning machine |
| Zdroj: | Journal of Sensors, Vol 2015 (2015) |
| ISSN: | 1687-7268 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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