Predicting Magnetic Field in Proximity of Power Transmission Lines using Artificial Neural Networks

M.A. Elhirbawy, A.M. Qabazard, and O.A. Al-Sayegh (Kuwait)


Artificial neural networks; magnetic field; memory; power transmission lines; reduction; simulation.


The work investigate the predictability of using flexible artificial neural networks for calculation the magnetic field in proximity of 5th ring road power transmission lines at state of Kuwait. This paper brings together important and vital concepts, calculations, test results, and case study. Artificial neural network has been employed, in order to predict the magnetic fields established by currents in normal and faulted transmission lines. Reductions in computation time and memory requirements have been achieved in this approach. The absolute mean value error percentage for estimating magnetic field was calculated to be 2.53% and 2.48% for the x-component and y-component, respectively. Three phase magnetic field calculation has been shown to be readily presented. The evaluation of the magnetic field in a wide range of configurations arising in practice can be done. Example of existing power transmission line shows practical applications.

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