A Stochastic Modeling and Artificial Neural Network-Based Method for Electric and Magnetic Field Reduction Near Overhead Transmission Lines

This paper considers the approach for overhead transmission lines’ (OHTL) electric and magnetic field reduction by finding the best phase conductors (PCs) and shield wires (SWs) positions. This approach combines an algorithm based on stochastic modeling for OHTL configuration generation, with the artificial neural networks (ANN) based method for the electric field strength and magnetic flux density determination. This approach enables the generation of an arbitrary number of different OHTL configurations, taking into account specific user-defined limitations. This further, enables to find the OHTL designs that are the best solution for the considered case study regarding electric and magnetic field levels. In this paper, a case study is presented where the considered approach is employed to find the OHTL designs that give the best results regarding the limitation of electric and magnetic field values.