Artificial neural networks and genetic algorithm modelling and identification of arc parameter in insulators flashover voltage and leakage current Online publication date: Mon, 10-Dec-2018
by Khaled Belhouchet; Abdelhafid Bayadi; M. Elhadi Bendib
International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 11, No. 1, 2019
Abstract: In this paper, we present an optimisation method based on genetic algorithms and artificial neural networks (ANN) experimental data from artificially polluted insulators for the determination of the arc constants and dielectric properties in the surface. The study of flashover phenomenon in polluted insulators has not yet been described accurately through a mathematical model. The definitions of arc constants are very difficult, which is created in the dry bands when the voltage exceeds its critical value. In this work, a pollution flashover generalised model is used. The obtained results show that the mathematical model with optimised arc constants simulates accurately the experimental data and corroborate the inverse relationship between flashover voltage and pre-flashover leakage current. For this purpose, an ANN was constructed in MATLAB and has been trained with several MATLAB training functions, while tests regarding the number of neurons, the number of epochs and the value of learning rate have taken place, in order to find which net architecture and which value of the other parameters give the best result. To validate our method an experimental tests for different insulators show very good agreement with the measured values and the computed ones.
Online publication date: Mon, 10-Dec-2018
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Aided Engineering and Technology (IJCAET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com