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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

The Estimation of the Critical Flashover Voltage of Insulators Using the Computational Intelligence

Author(s): Louiza Dehyadegari* and Somayeh Khajehasani

Volume 13, Issue 4, 2020

Page: [550 - 556] Pages: 7

DOI: 10.2174/2213275912666181219093455

Price: $65

Abstract

Background: Electric insulation is generally a vital factor in both the technical and economic feasibility of complex power and electronic systems. Several researches focus on the behavior of insulators under polluted conditions. That they are mathematical and physical models of insulators, experiments and simulation programs. Also experiments on critical flashover voltage are timeconsuming and have more limitations such as high cost and need for especial equipment’s.

Objective: This paper focused on optimized predicting of critical flashover voltage of Polluted insulators based on artificial intelligence.

Method: Fuzzy logic and artificial neural networks are used in order to have the best estimation of the critical flashover.

Results: In this way the correlation index (regression coefficient) improved about 2% toward previous works with same experimental data sets. Additionally, with using the properties of nonlinear artificial neural networks we can have the perfect (R=100%) prediction of the critical flashover voltage on experimental dataset.

Conclusion: In this paper two methods for the estimation of critical flashover voltage of polluted insulators using fuzzy logic and neural networks was presented. the regression coefficient R achieved by the optimal parameters is 98.4% while in previous work is 96.7%. In neural network model we have regression coefficient 100% and in previous neural network model it was 99%. our test set is the same as previous works and achieved from experiments. These results show that fuzzy proposed methods are powerful and useful tools lead to a more accurate, generalized and objective estimation of the critical flashover voltage.

Keywords: Critical flashover voltage, fuzzy logic, neural networks, polluted insulators, regression coefficient, critical flashover voltage.

Graphical Abstract

[1]
M.R. Raghuveer . "Numerical prediction of HVDC wall bushing flashover caused by uneven wetting", Proceedings of Conference on Electrical Insulation and Dielectric Phenomena - CEIDP 96 CEIDP-96 1996.
[2]
A. Chaudhuri , K. Mandaviya , P. Badelia . Soumya and K. Ghosh. “Soft computing techniques for optical character recognition systems".In: Optical Character Recognition Systems for Different Languages with Soft Computing. Cham: Springer 2017; pp. 43-83.
[3]
F.V. Topalis , I.F. Gonos , and I.A. Stathopoulos . "Dielectric behavior of polluted porcelain insulators," In IEEE Proceedings Generation Transmission and Distribution, July 2001; Vol. 148, pp. 269-274.
[4]
A. Salem , R.A. Rahman , M.S. Kamarudin , and N.A. Othman . ,"Factors and models of pollution flashover on high voltage outdoor insulators: Review", 2017; IEEE Conference on Energy Conversion (CENCON) June ; 2017; pp. 241-6.
[5]
L. Boukezzi , L. Bessissa , A. Boubakeur , and D. Mahi . ,"Neural networks and fuzzy logic approaches to predict mechanical properties of XLPE insulation cables under thermal aging". Neural Comp Appl Vol.28, pp. 3557-3570, 2017.
[6]
A.G. Suresh , and P. Dixit . ",ANN model to predict critical flashover voltages of polluted porcelain disc insulators", Int J Appl Eng Res Vol. 12, pp. 2942-2951, 2017.
[7]
V.T. Kontargyri , A.A. Gialketsi , G.J. Tsekouras , I.F. Gonos , and I.A. Stathopoulos . ,"Design of an artificial neural network for the estimation of the flashover voltage on insulators", Electric Power Syst Res pp. 1532–1540, 2007.
[8]
E. Karampotsis . K. Boulas1, A. Tzanetos, V. P. Androvitsaneas, I.F. Gonos, G. Dounias, and I. A. Stathopoulos, “Computational intelligence techniques for modelling the critical flashover voltage of insulators: from accuracy to comprehensibility", In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Springer: Cham, pp 295-301.2017;
[9]
G.E. Asimakopoulou , V.T. Kontaragyri , G.J. Tsekouras , C.N. Elias , F.E. Asimakopoulou , and I.A. Stathopoulos . ,"A fuzzy logic optimization methodology for the estimation of critical flashover voltage on insulators". Electric Power Syst Res Vol. 81, pp. 580-588, 2011
[10]
K. Erenturk . ,"Adaptive-network-based fuzzy inference system application to estimate the flashover voltage on insulator", Instrument Sci Tech Vol. 37, pp. 446–461, 2009.
[11]
A. Chatterjee , and A. Rakshit . ,"Electric field optimization of HV electrode systems", IEEE Trans Dielec Electric Insul Vol. 8, pp.: 995-1002. 2001
[12]
K. Erenturk . "Adaptive-Network-Based fuzzy inference system application to estimate the flashover voltage on insulator", Instrument Sci Tech , Vol. 37, No.4, pp.: 446-61. 2009.
[13]
G.E. Asimakopoulou , V.T. Kontaragyri , G.J. Tsekouras , F.E. Asimakopoulou , I.F. Gonos , I.A. Stathopoulos . ,"Artificial network optimization methodology for the estimation of the critical flashover voltage on insulators", IET Science. 90-104. 2009
[14]
S. Russell , and P. Norvig . ,"The most popular method for learning in multilayer networks is called Back-propagation," Artif Intell Mod Approach, pp. 578-86. 1980
[15]
A.E. Bryson , and Y.C. Ho . ,"Applied optimal control: Optimization, estimation, and control," Waltham, Massachusetts: Blaisdell Publishing Company, pp. 481-92. 1969
[16]
K. Ikonomou , G. Katsibokis , A. Kravaritis , and I.A. Stathopoulos . ,"Cool fog tests on artificially polluted suspension insulators," 5th International Symposium on High Voltage Engineering Braunschweig 1987; Vol. 2, pp.: 52-8.
[17]
R. Sundararajan , N.R. Sadhureddy , R.S. Gorur . ,"Computer-aided design of porcelain insulators under polluted conditions," IEEE Trans Dielectr Electric Insul Vol. 2, pp: 121-7. 1995

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