ARTIFICIAL NEURAL NETWORK MODELS FOR ESTIMATION OF ELECTRIC FIELD INTENSITY AND MAGNETIC FLUX DENSITY IN THE PROXIMITY OF OVERHEAD TRANSMISSION LINE

This paper considers the application of artificial neural network (ANN) models for electric field intensity and magnetic flux density estimation in the proximity of overhead transmission lines. Specifically, two distinct ANN models are used to facilitate independent estimation of electric field intensity and magnetic flux density in the proximity of overhead transmission lines. The considered ANN approach is systematically evaluated under different scenarios. An example of an overhead transmission line with horizontal phase conductor configuration is used to enable a direct comparison of the electric field intensity and magnetic flux density estimates generated by the two ANN models to measurement results obtained over the lateral profile. Further investigation of ANN models involves an extensive study whereby 13 different overhead transmission lines of horizontal configurations are used as the basis for comparing measurement results to estimates provided by the ANN models. In this study, the performance analysis of the ANN models was evaluated using coefficient of determination and root mean square error. The obtained results demonstrate that the considered ANN approach can be used to estimate the electric field intensity and magnetic flux density in the proximity of overhead transmission lines.