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| Titre: | Choice of input data type of artificial neural network to detect faults in alternative current systems |
| Auteur(s): | Benslimane, T.
Chetate, Boukhmis |
| Mots-clés: | Diagnosis
Learning Data type
AC voltage and current
RMS value
Instantaneous value
Average value |
| Date de publication: | 2006 |
| Collection/Numéro: | American Journal of Applied Sciences/ Vol.3, N°8 (2006);p.p. 1979-1983 |
| Résumé: | This paper present a study on different input data types of ANN used to detect faults such as overvoltage in AC systems (AC network , induction motor). The input data of ANN are AC voltage and current. In no fault condition, voltage and current are sinusoidal. The input data of the ANN may be the instantaneous values of voltage and current, their RMS values or their average values after been rectified. In this paper we presented different characteristics of each one of these data. A digital software C++ simulation program was developed and simulation results were presented |
| URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/jspui/handle/123456789/249 |
| ISSN: | 1546-9239 |
| Collection(s) : | Publications Internationales
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