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A Metamodeling Approach for Leader Progression Model-based Shielding Failure Rate Calculation of Transmission Lines Using Artificial Neural Networks
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 Title & Authors
A Metamodeling Approach for Leader Progression Model-based Shielding Failure Rate Calculation of Transmission Lines Using Artificial Neural Networks
Tavakoli, Mohammad Reza Bank; Vahidi, Behrooz;
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 Abstract
The performance of transmission lines and its shielding design during a lightning phenomenon are quite essential in the maintenance of a reliable power supply to consumers. The leader progression model, as an advanced approach, has been recently developed to calculate the shielding failure rate (SFR) of transmission lines using geometrical data and physical behavior of upward and downward lightning leaders. However, such method is quite time consuming. In the present paper, an effective method that utilizes artificial neural networks (ANNs) to create a metamodel for calculating the SFR of a transmission line based on shielding angle and height is introduced. The results of investigations on a real case study reveal that, through proper selection of an ANN structure and good training, the ANN prediction is very close to the result of the detailed simulation, whereas the Processing time is by far lower than that of the detailed model.
 Keywords
Shielding failure rate;Metamodel;Artificial neural network;Lightning performance;Leader progression model;Charge simulation method;
 Language
English
 Cited by
1.
An Analytical Method for Estimation of Lightning Performance of Transmission Lines Based on a Leader Progression Model, IEEE Transactions on Electromagnetic Compatibility, 2014, 56, 6, 1530  crossref(new windwow)
 References
1.
Y. Carson and A. Maria, Simulation optimization Methods & Applications, In Winter Simulation Conference, p. 118-126, 1993.

2.
K. Akbay, "Using Simulation Optimization to Find the Best Solution," IIE Solutions Vol. 28, p. 24-29, 1996.

3.
B. Vahidi, M. R. Bank Tavakoli and S. H. Hosseinian, "Determining Best Positions for Surge Arresters in Power System for Lightning Shielding Failure Protection Using Simulation Optimization Approach," Euro. Trans. on Electrical Power, Vol. 20, Issue 3, p. 255-276, April 2010. crossref(new window)

4.
B. Vahidi, M. R. Bank Tavakoli and S. H. Hosseinian, "Evaluating Optimum Arrester's Locations in HV and EHV Networks Using Simulation Optimization to Suppress Switching Surge Overvoltages," Amirkabir Journal of Sci. and Tech., Vol. 18, p. 35-44, 2007.

5.
IEEE Guide for Improving the Lightning Performance of Transmission Lines, IEEE Std. 1243, 1997.

6.
F. S. Young, J. M. Clayton and A. R. Hileman, "Shielding of transmission lines," IEEE Trans. Power App. Syst., Vol. 82, p. 132-154, 1963.

7.
A. J. Erikson, "An improved electrogeometric model for transmission line shielding analysis," IEEE Trans. Power Delivery, Vol. 2, p. 871-886, 1987. crossref(new window)

8.
B. Vahidi, M. Yahyaabadi, M. R. Bank Tavakoli and S. M. Ahadi, "Leader progression analysis model for shielding failure computation by using charge simulation method," IEEE Trans. on Power Delivery, Vol. 23, p. 2201-2206, 2008. crossref(new window)

9.
L. Dellera, and E. Garbagnati, "Lightning stroke simulation by means of the leader progression model, Part I: Description of the model and evaluation of exposure of free shielding structures," IEEE Trans. Power Delivery, Vol. 5, 2009-2022, 1990. crossref(new window)

10.
H. Singer, H. Steinbigler and P. Weiss, "A charge simulation method for the calculation of high voltage fields," IEEE Trans. Power App. Syst., Vol. PAS-93, 1660-1668, 1974. crossref(new window)

11.
N. H. Malik, "A review of the charge simulation method and its application," IEEE Trans. on EI, Vol. 24, p. 3-20, 1989.

12.
E. M. Bazelyan and Y. P. Raizer, Lightning Physics and Lightning Protection, Institute of Physics Pub., Bristol, 2000.

13.
P. Lalande, A. Bondiou, G. Bacchiega, and I. Gallimberti, "Observations and modeling of lightning leaders," C. R. Phys., Vol. 3, p. 1375-1392, 2002. crossref(new window)

14.
K. Berger, "Novel observations on lightning discharges: results of research on mount San Salvatore," J. Franklin Inst., Vol. 283, p. 478-525, 1967. crossref(new window)

15.
M. Becerra and V. Cooray, "A simplified physical model to determine the lightning upward connecting leader inception," IEEE Trans. Power Del., Vol. 21, p. 897-908, 2006. crossref(new window)

16.
F. A. M. Rizk, "Modeling of lightning incidence to tall structures part I: Theory," IEEE Trans. Power Del., Vol. 9, p. 162-171, 1994. crossref(new window)

17.
U. Kumar, P. K. Bokka, and J. Padhii, "A macroscopic inception criterion for the upward leaders of natural lightning," IEEE Trans. Power Delivery, Vol. 20, p. 904-911, 2005. crossref(new window)

18.
I. Gallimberti, "The mechanism of long spark formation," J. Physique Coll., Vol. 40, p. 193-250, 1972.

19.
J. G. Anderson, Lightning performance of transmission lines, Transmission line reference book 345 kV and above, chapter 12, EPRI, 1982.

20.
A. J. Erikson, "The incidence of lightning strikes to power lines," IEEE Trans. Power Delivery, Vol. 2, p. 859-870, 1987. crossref(new window)

21.
C. M. Bishop, Neural network for pattern recognition, Oxford University Press: Oxford, 1995.

22.
H. M. Hornik, M. Stinchcombe and H. White, "Multilayer feed forward networks are universal approximators," Neutral Networks, Vol. 2, p. 359-366, 1989. crossref(new window)

23.
D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning representations by back-propagating errors," Nature, Vol. 323, p. 533-536, 1986. crossref(new window)