Artificial Neural Network Approach to Electric Field Approximation Around Overhead Power Transmission Lines

A. Oonsivilai, R. Boonwutiwiwat, T. Kulworawanichpong, and P. Pao-La-Or (Thailand)


electric field strength, finite difference method (FDM), finite element method (FEM), artificial neural network (ANN), boundary conditions, estimation


This paper presents the use of artificial neural networks (ANN) to estimate electric fields around an overhead power transmission line. Although, there exist many efficient numerical methods, e.g. finite difference method (FDM), finite element method (FEM), boundary element method (BEM), etc, to estimate electric field distribution caused by live conductors, it typically consumes substantial execution time when high accuracy of obtained solutions is required or especially when time varying field is involved. Therefore, to estimate the electric field strength using ANN employing feedforword network with backpropagation learning can be an alternative. To evaluate its use, overhead 22-kV single phase power line of 100 m2 test area and 230-kV three phase power lines of 400 m2 test area were simulated. The results obtained from the ANN are compared with those obtained by the analytical method, the FDM and the FEM.

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