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

[1]     G. Draper, T. Vincent, ME. Kroll, J. Swanson, “Childhood cancer in relation to distance from high voltage power lines in England and Wales: a case-control study”; BMJ, Vol. 330, pp. 1-5, 2005.##
[2]     S. Ozen, “Low-frequency transient electric and magnetic fields coupling to child body”;  Radiat Prot Dosimetry, Vol. 128, pp. 62-67, 2008.##
[3]     IC. Ahlbom, E. Cardis, A. Green, M. Line, “ Review of the epidemiologic literature on EMF and health. Environ” Health Perspect, Vol. 109, pp. 911-933, 2001.##
[4]     S. Helhel, S. Ozen, “ Assessment of occupational exposure to magnetic fields in the high voltage substations(154/34.5kV)”  Radiat Prot Dosimetry, Vol. 128, pp. 464-470, 2008.##
[5]     D.Noble, A. McKinlay, M. Repacholi, “Effects of static magnetic Fields relevant to human health” Prog Biophys MolBio, Vol. 87, pp. 171-372, 2005.##
[6]     IARC. “ Monographs on the evaluation of carcinogenic risks to humans, Non-ionizing radiation, Part 1: Static and extremely low-frequency (ELF) electric and magnetic fields” International Agency for Research on Cancer, Lyon, France, 2002.##
[7]     Li DK, R. Odouli, S. Wi, T. Janevic, I. Golditch, T. Dan, Bracken, R. Senio, R. Ranki, R. Iriye “A population-based perspective cohort study of personal exposure to magnetic fields during pregnancy and the risk of miscarriage” Epidemiology, Vol. 13, PP. 9-20, 2002.##
[8]     S. Ozen, “Evaluation and measurement of magnetic field exposure at a typical high-voltage substation and its power lines” Radiat Prot Dosim, Vol. 128, PP. 198–205, 2008.##
[9]     F. Moro, R. Turri, “Fast analytical computation of       power-line magnetic fields by complex vector method” IEEE Trans Power Deliver, Vol. 23, PP.1042–8, 2008.##
 [10]   G. Filippopoulos, D. Tsanakas, “ Analytical calculation of the magnetic field produced by electric power lines” IEEE Trans Power Deliver. Vol. 20, PP. 1474–82, 2005.##
[11]  NH. Malik, “ A review of the charge simulation method and its applications” IEEE Trans Electr Insul, Vol. 24, PP. 3–20, 1989.##
[12]  A.Ayad, W.Krika, H.Boudjell, F.Benhamida, A.Horch,” Simulation of the Electromagnetic Field in the Vicinity of the Overhead Power Transmission Line,” European Journal of Electrical Engineering, Vol. 21, pp. 49-53, 2019.##
[13]  I. Kosalay, “Fuzzy logic based ELF magnetic field estimation in substations” Radiat Prot Dosim, Vol.131, PP. 240–50, 2008.##
[14]  L. Ekonomou, VT. Kontargyri, St. Kourtesi, TI. Maris, IA. Stathopulos, “ Artificial neural networks in high voltage transmission line problems” Meas Sci Technol Vol. 18, PP. 2239–44, 2007.##
[15]  K. Hornik, M. Stinchcombe, H.White, “ Multilayer feedforward networks are universal approximators”  Neural Network Vol. 2, PP. 359–66, 1989.##
[16]  J. Park, IW. Sandberg, “Universal approximation using radial basis function networks”  Neural Comp, Vol. 3, PP. 246–57, 1991.##
[17]  PN. Charalambos, PP. Antonis, AR. Panos, AK. George, NS. John, “Measurements and predictions of electric and magnetic fields from power lines” Electr Pow Syst Res, Vol.81, PP. 1107-1116, 2011.##
[18]  R. Vesna, R. Jasna, “Prediction of magnetic field near power lines by normalized radial basis function network” Adv Eng Softw, Vol. 42, PP. 934-938, 2011.##
[19]  F. Munoz, JA. Aguado, F. Marti, JJ. Lopez, A. Rodrigue, JB. Garcia, AR. Treitero, R. Molin, “An intelligent computing technique to estimate the magnetic _eld generated by overhead transmission lines using a hybrid GA-Sx algorithm” Int J Elec Power, Vol. 53, PP. 43-53, 2013.##
[20]  H.F. CARLAK, S. Ozen, S. Bilgin, “Low-frequency exposure analysis using electric and magnetic _eld measurements and predictions in the proximity of power transmission lines in urban areas”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 25, PP. 3994 -4005, 2017.##
[21]  F. Carlak, S. Ozen. "Prediction of the Electric Field Emissions around the High-voltage Power Lines using Neural Network Algorithms." Proceedings of the 2nd World Congress on Electrical Engineering and Computer Systems and Science (EECSS'16), Budapest, Hungary – August       16 – 17, 2016.##
[22]  G.A. Kulkarni,, and W. Z. Gandhare. "Prediction of Electromagnetic Fields around High Voltage Transmission Lines." Acta Technica Jaurinensis, Vol. 10, PP. 50-58, 2017.##
[23]  K. R. Milani, “Distribution network overhead line, Aparatuse and Design”; Applied Science University of Sanat Abo Bargh, Press: Ministry of Energy, 1381, (in Persian).##
[24]  AC-DC module user’s guide, www.comsol.com/patents, 2018.##
[25]  C. Bishop, “Improving the generalization properties of radial basis function neural networks. Neural Computation,” Neural computation Vol.3, PP.579-588, 1991.##
[26]  L. Grabec, "The normalized radial basis function neural network and its relation to the perceptron." arXiv preprint hysics/0703229, 2007.##
Applied Electromagnetics
Volume 8, Issue 1 - Serial Number 20
September 2020
Pages 107-116

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History

  • Receive Date: 18 November 2020
  • Accept Date: 18 November 2020
  • Publish Date: 22 August 2020

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