Comparison of some neural network algorithms used in prediction of XLPE HV insulation properties under thermal aging

Author

Boukezzi, Larbi ; Boubakeur, A.

Author_Institution

Mater. Sci. & Inf. Labortory, Djelfa Univ., Djelfa, Algeria

fYear

2012

fDate

23-27 Sept. 2012

Firstpage

1218

Lastpage

1222

Abstract

Some Artificial neural network algorithms have been used to predict properties of high voltage electrical insulation under thermal aging in term to reduce the aging experiment time. In this paper we present a short comparison of the obtained results in the case of Cross-linked Polyethylene (XLPE). The theoretical and the experimental results are concordant. As a neural network application, we propose a new method based on Radial Basis Function Gaussian network (RBFG) trained by two algorithms: Random Optimization Method (ROM) and Back-propagation (BP).

Keywords

XLPE insulation; ageing; backpropagation; optimisation; power engineering computing; radial basis function networks; XLPE HV insulation properties prediction; artificial neural network algorithms; back-propagation; concordant; cross-linked polyethylene; high voltage electrical insulation; neural network application; radial basis gaussian network; random optimization method; thermal aging; Aging; Artificial neural networks; Insulation; Prediction algorithms; Read only memory; Training; Neural network; Prediction; XLPE insulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Condition Monitoring and Diagnosis (CMD), 2012 International Conference on

Conference_Location

Bali

Print_ISBN

978-1-4673-1019-2

Type

conf

DOI

10.1109/CMD.2012.6416381

Filename

6416381