DocumentCode :
2520033
Title :
Identification of partial discharges sources using a combination of linear prediction and neural networks
Author :
Medjeldi, T. ; Nemamcha, M. ; Gosse, J.P.
Author_Institution :
Centre Univ. de Guelma, Algeria
fYear :
1998
fDate :
22-25 Jun 1998
Firstpage :
165
Lastpage :
167
Abstract :
The ability to recognize forms by means of neural networks combined with a linear prediction analyze has been developed in this paper for partial discharges (PD) sources recognition. The PD sources are artificially created on a Teflon cell having two armatures isolated by polypropylene films. This experiments have been made at LEMD/CNRS Grenoble (France). This first work has been carried out by the use of a sample of seven specimens representing seven cases of presence of cavities on the cell insulation of study. The seven defects represented by respective signals of apparent charge were processed modeled by linear prediction and then passed to a back propagation neural networks
Keywords :
backpropagation; neural nets; partial discharges; prediction theory; Teflon cell; backpropagation; cavity; defect; form recognition; insulation; linear prediction; neural network; partial discharges source identification; polypropylene film; Artificial neural networks; Biological neural networks; Electrodes; Fault location; Insulation; Neural networks; Partial discharges; Signal processing; Time measurement; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Conduction and Breakdown in Solid Dielectrics, 1998. ICSD '98. Proceedings of the 1998 IEEE 6th International Conference on
Conference_Location :
Vasteras
Print_ISBN :
0-7803-4237-2
Type :
conf
DOI :
10.1109/ICSD.1998.709251
Filename :
709251
Link To Document :
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