Prediction of magnetic field near power lines by normalized radial basis function network

Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. This work involved the computation of the magnetic field generated by 110 kV overhead power lines using a normalized radial basis function (NRBF) network. Training of the evolving NRBF network is achieved by using the data generated from the numerical simulation based on Charge Simulation method (CSM). Then, NRBF has been used to determine the magnetic field distribution in a new geometry differing from the geometries used for training. These test results show that proposed NRBF network can be used as useful tool to calculate the magnetic fields from power lines, alternative to the conventional methods.

► We construct the radial basis neural network models to predict the magnetic field of power lines.
► Data for training the neural network is obtained from the numerical simulations.
► The developed NRBF model makes it possible to determine the magnetic fields easier.
► NRBF model provides considerable reduction of analysis time.
► The proposed method ensures accept able accuracy and satisfying convergence.