An intelligent computational method to estimate the electric and magnetic field power frequency of distribution network using neural network based on normalized radial basis functions

Document Type : Original Article

Authors

1 University of Science and Technology of Mazandaran

2 Faculty of Electrical and Robotic Engineering, Shahrood University of Technology

Abstract

Population growth in urban areas and the rising demand for electricity has led to the expansion of the electricity grid, more loading of power transmission lines and line privacy reduction. Due to such conditions in residential and work environments, the probability of electric and magnetic fields exposure has increased. Since exposure to electromagnetic fields at power frequency has undesirable effects on human health, this has caused a serious challenge. To gain knowledge as to how electromagnetic fields are emitted, the artificial intelligence technique has been considered as an accurate and fast method for the required electric and magnetic fields modeling. In this paper, a neural network based on normalized radial basis functions has been used to estimate the electric fields and magnetic flux density. The required data for proposed model training and validation have been extracted based on five different layouts of 20kV distribution network lines in COMSOL software. Based on the performed simulations, values of the electric fields and the magnetic flux densities in different longitudinal and transverse coordinates of the space around the lines have been measured. Comparison of the estimated and measured results has shown that the proposed model has a very good accuracy for electric field and magnetic flux density determination at different points around the lines for different structures of the distribution network

Keywords

References

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Applied Electromagnetics
Volume 8, Issue 1 - Serial Number 20
September 2020
Pages 107-116

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  • Receive Date: 18 November 2020
  • Accept Date: 18 November 2020

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