| Full text | |
| Author(s): |
Souza, Andre N.
[1]
;
da Costa, Jr., Pedro
[1]
;
da Silva, Paulo S.
[1]
;
Ramos, Caio C. O.
[2]
;
Papa, Joao P.
[3]
Total Authors: 5
|
| Affiliation: | [1] Sao Paulo State Univ, Dept Elect Engn, Bauru - Brazil
[2] Univ Sao Paulo, Dept Elect Engn, Sao Paulo - Brazil
[3] Sao Paulo State Univ, Dept Comp, Bauru - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | APPLIED ARTIFICIAL INTELLIGENCE; v. 26, n. 5, p. 503-515, 2012. |
| Web of Science Citations: | 0 |
| Abstract | |
In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification. (AU) | |
| FAPESP's process: | 10/12398-0 - Development and evaluation of fault location techniques in underground distributions systems |
| Grantee: | André Nunes de Souza |
| Support Opportunities: | Regular Research Grants |