Artificial neural network methodology for the estimation of ground resistance

Authors:
F. E. Asimakopoulou;E. A. Kourni;V. T. Kontargyri;G. J. Tsekouras;I. A. Stathopulos
Affiliations:
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Department of Electrical Engineering and Computer Science, Hellenic Naval Academy, Piraeus, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Venue:
Proceedings of the 15th WSEAS international conference on Systems
Year:
2011

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Abstract

Aim of this paper is the estimation of the variation of ground resistance throughout the year by using Artificial Neural Networks. Based on measurements of soil resistivity, temperature, and rainfall during a period of time, various algorithms for training Artificial Neural Networks have been tested regarding their ability to predict the ground resistance. In order for the parameters of each training algorithm to be selected; an optimization procedure has been followed. The effectiveness of the Artificial Neural Network is proved through the high correlation index between the estimated and the measured values of the ground resistance.